{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tensorflow进行数据分析\n",
    "简单介绍了Tensorflow深度学习框架的运算流程之后，引入一个具体案例，并使用Tensorflow对数据进行 分析。\n",
    "\n",
    "在数据分类的研究中，普遍存在类别分布不平衡的问题，即某一类别的样本数量远远多于另一类，具有这样特征的数据集视为不平衡。  \n",
    "我们将使用Kaggle 上托管的 Credit Card Fraud Detection 数据集，目的是从总共 284,807 笔交易中检测出仅有的 492 笔欺诈交易。  \n",
    "我们需要做的就是定义模型和类权重，从而帮助模型从不平衡数据中学习。\n",
    "\n",
    "具体流程：\n",
    "* 使用 Pandas 加载 CSV 文件。\n",
    "* 创建训练、验证和测试集。\n",
    "* 训练模型（包括设置类权重）。\n",
    "* 使用各种指标（包括精确率和召回率）评估模型。\n",
    "* 尝试使用常见技术来处理不平衡数据，例如：类加权"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "\n",
    "import os\n",
    "import tempfile\n",
    "\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "\n",
    "import sklearn\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "# 控制图像属性\n",
    "mpl.rcParams['figure.figsize'] = (12, 10)\n",
    "colors = plt.rcParams['axes.prop_cycle'].by_key()['color']"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 数据处理与浏览"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>V1</th>\n",
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       "      <th>V23</th>\n",
       "      <th>V24</th>\n",
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       "      <th>V26</th>\n",
       "      <th>V27</th>\n",
       "      <th>V28</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Class</th>\n",
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       "      <td>0.128539</td>\n",
       "      <td>-0.189115</td>\n",
       "      <td>0.133558</td>\n",
       "      <td>-0.021053</td>\n",
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       "      <td>-0.082361</td>\n",
       "      <td>-0.078803</td>\n",
       "      <td>0.085102</td>\n",
       "      <td>-0.255425</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.225775</td>\n",
       "      <td>-0.638672</td>\n",
       "      <td>0.101288</td>\n",
       "      <td>-0.339846</td>\n",
       "      <td>0.167170</td>\n",
       "      <td>0.125895</td>\n",
       "      <td>-0.008983</td>\n",
       "      <td>0.014724</td>\n",
       "      <td>2.69</td>\n",
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       "      <td>-1.358354</td>\n",
       "      <td>-1.340163</td>\n",
       "      <td>1.773209</td>\n",
       "      <td>0.379780</td>\n",
       "      <td>-0.503198</td>\n",
       "      <td>1.800499</td>\n",
       "      <td>0.791461</td>\n",
       "      <td>0.247676</td>\n",
       "      <td>-1.514654</td>\n",
       "      <td>...</td>\n",
       "      <td>0.247998</td>\n",
       "      <td>0.771679</td>\n",
       "      <td>0.909412</td>\n",
       "      <td>-0.689281</td>\n",
       "      <td>-0.327642</td>\n",
       "      <td>-0.139097</td>\n",
       "      <td>-0.055353</td>\n",
       "      <td>-0.059752</td>\n",
       "      <td>378.66</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
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       "      <td>-0.966272</td>\n",
       "      <td>-0.185226</td>\n",
       "      <td>1.792993</td>\n",
       "      <td>-0.863291</td>\n",
       "      <td>-0.010309</td>\n",
       "      <td>1.247203</td>\n",
       "      <td>0.237609</td>\n",
       "      <td>0.377436</td>\n",
       "      <td>-1.387024</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.108300</td>\n",
       "      <td>0.005274</td>\n",
       "      <td>-0.190321</td>\n",
       "      <td>-1.175575</td>\n",
       "      <td>0.647376</td>\n",
       "      <td>-0.221929</td>\n",
       "      <td>0.062723</td>\n",
       "      <td>0.061458</td>\n",
       "      <td>123.50</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.0</td>\n",
       "      <td>-1.158233</td>\n",
       "      <td>0.877737</td>\n",
       "      <td>1.548718</td>\n",
       "      <td>0.403034</td>\n",
       "      <td>-0.407193</td>\n",
       "      <td>0.095921</td>\n",
       "      <td>0.592941</td>\n",
       "      <td>-0.270533</td>\n",
       "      <td>0.817739</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.009431</td>\n",
       "      <td>0.798278</td>\n",
       "      <td>-0.137458</td>\n",
       "      <td>0.141267</td>\n",
       "      <td>-0.206010</td>\n",
       "      <td>0.502292</td>\n",
       "      <td>0.219422</td>\n",
       "      <td>0.215153</td>\n",
       "      <td>69.99</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
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       "      <td>2.0</td>\n",
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       "      <td>0.960523</td>\n",
       "      <td>1.141109</td>\n",
       "      <td>-0.168252</td>\n",
       "      <td>0.420987</td>\n",
       "      <td>-0.029728</td>\n",
       "      <td>0.476201</td>\n",
       "      <td>0.260314</td>\n",
       "      <td>-0.568671</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.208254</td>\n",
       "      <td>-0.559825</td>\n",
       "      <td>-0.026398</td>\n",
       "      <td>-0.371427</td>\n",
       "      <td>-0.232794</td>\n",
       "      <td>0.105915</td>\n",
       "      <td>0.253844</td>\n",
       "      <td>0.081080</td>\n",
       "      <td>3.67</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>4.0</td>\n",
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       "      <td>0.141004</td>\n",
       "      <td>0.045371</td>\n",
       "      <td>1.202613</td>\n",
       "      <td>0.191881</td>\n",
       "      <td>0.272708</td>\n",
       "      <td>-0.005159</td>\n",
       "      <td>0.081213</td>\n",
       "      <td>0.464960</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.167716</td>\n",
       "      <td>-0.270710</td>\n",
       "      <td>-0.154104</td>\n",
       "      <td>-0.780055</td>\n",
       "      <td>0.750137</td>\n",
       "      <td>-0.257237</td>\n",
       "      <td>0.034507</td>\n",
       "      <td>0.005168</td>\n",
       "      <td>4.99</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7.0</td>\n",
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       "      <td>1.417964</td>\n",
       "      <td>1.074380</td>\n",
       "      <td>-0.492199</td>\n",
       "      <td>0.948934</td>\n",
       "      <td>0.428118</td>\n",
       "      <td>1.120631</td>\n",
       "      <td>-3.807864</td>\n",
       "      <td>0.615375</td>\n",
       "      <td>...</td>\n",
       "      <td>1.943465</td>\n",
       "      <td>-1.015455</td>\n",
       "      <td>0.057504</td>\n",
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       "      <td>-0.051634</td>\n",
       "      <td>-1.206921</td>\n",
       "      <td>-1.085339</td>\n",
       "      <td>40.80</td>\n",
       "      <td>0</td>\n",
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       "      <th>8</th>\n",
       "      <td>7.0</td>\n",
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       "      <td>0.286157</td>\n",
       "      <td>-0.113192</td>\n",
       "      <td>-0.271526</td>\n",
       "      <td>2.669599</td>\n",
       "      <td>3.721818</td>\n",
       "      <td>0.370145</td>\n",
       "      <td>0.851084</td>\n",
       "      <td>-0.392048</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.073425</td>\n",
       "      <td>-0.268092</td>\n",
       "      <td>-0.204233</td>\n",
       "      <td>1.011592</td>\n",
       "      <td>0.373205</td>\n",
       "      <td>-0.384157</td>\n",
       "      <td>0.011747</td>\n",
       "      <td>0.142404</td>\n",
       "      <td>93.20</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9.0</td>\n",
       "      <td>-0.338262</td>\n",
       "      <td>1.119593</td>\n",
       "      <td>1.044367</td>\n",
       "      <td>-0.222187</td>\n",
       "      <td>0.499361</td>\n",
       "      <td>-0.246761</td>\n",
       "      <td>0.651583</td>\n",
       "      <td>0.069539</td>\n",
       "      <td>-0.736727</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.246914</td>\n",
       "      <td>-0.633753</td>\n",
       "      <td>-0.120794</td>\n",
       "      <td>-0.385050</td>\n",
       "      <td>-0.069733</td>\n",
       "      <td>0.094199</td>\n",
       "      <td>0.246219</td>\n",
       "      <td>0.083076</td>\n",
       "      <td>3.68</td>\n",
       "      <td>0</td>\n",
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      ],
      "text/plain": [
       "   Time        V1        V2        V3        V4        V5        V6        V7  \\\n",
       "0   0.0 -1.359807 -0.072781  2.536347  1.378155 -0.338321  0.462388  0.239599   \n",
       "1   0.0  1.191857  0.266151  0.166480  0.448154  0.060018 -0.082361 -0.078803   \n",
       "2   1.0 -1.358354 -1.340163  1.773209  0.379780 -0.503198  1.800499  0.791461   \n",
       "3   1.0 -0.966272 -0.185226  1.792993 -0.863291 -0.010309  1.247203  0.237609   \n",
       "4   2.0 -1.158233  0.877737  1.548718  0.403034 -0.407193  0.095921  0.592941   \n",
       "5   2.0 -0.425966  0.960523  1.141109 -0.168252  0.420987 -0.029728  0.476201   \n",
       "6   4.0  1.229658  0.141004  0.045371  1.202613  0.191881  0.272708 -0.005159   \n",
       "7   7.0 -0.644269  1.417964  1.074380 -0.492199  0.948934  0.428118  1.120631   \n",
       "8   7.0 -0.894286  0.286157 -0.113192 -0.271526  2.669599  3.721818  0.370145   \n",
       "9   9.0 -0.338262  1.119593  1.044367 -0.222187  0.499361 -0.246761  0.651583   \n",
       "\n",
       "         V8        V9  ...       V21       V22       V23       V24       V25  \\\n",
       "0  0.098698  0.363787  ... -0.018307  0.277838 -0.110474  0.066928  0.128539   \n",
       "1  0.085102 -0.255425  ... -0.225775 -0.638672  0.101288 -0.339846  0.167170   \n",
       "2  0.247676 -1.514654  ...  0.247998  0.771679  0.909412 -0.689281 -0.327642   \n",
       "3  0.377436 -1.387024  ... -0.108300  0.005274 -0.190321 -1.175575  0.647376   \n",
       "4 -0.270533  0.817739  ... -0.009431  0.798278 -0.137458  0.141267 -0.206010   \n",
       "5  0.260314 -0.568671  ... -0.208254 -0.559825 -0.026398 -0.371427 -0.232794   \n",
       "6  0.081213  0.464960  ... -0.167716 -0.270710 -0.154104 -0.780055  0.750137   \n",
       "7 -3.807864  0.615375  ...  1.943465 -1.015455  0.057504 -0.649709 -0.415267   \n",
       "8  0.851084 -0.392048  ... -0.073425 -0.268092 -0.204233  1.011592  0.373205   \n",
       "9  0.069539 -0.736727  ... -0.246914 -0.633753 -0.120794 -0.385050 -0.069733   \n",
       "\n",
       "        V26       V27       V28  Amount  Class  \n",
       "0 -0.189115  0.133558 -0.021053  149.62      0  \n",
       "1  0.125895 -0.008983  0.014724    2.69      0  \n",
       "2 -0.139097 -0.055353 -0.059752  378.66      0  \n",
       "3 -0.221929  0.062723  0.061458  123.50      0  \n",
       "4  0.502292  0.219422  0.215153   69.99      0  \n",
       "5  0.105915  0.253844  0.081080    3.67      0  \n",
       "6 -0.257237  0.034507  0.005168    4.99      0  \n",
       "7 -0.051634 -1.206921 -1.085339   40.80      0  \n",
       "8 -0.384157  0.011747  0.142404   93.20      0  \n",
       "9  0.094199  0.246219  0.083076    3.68      0  \n",
       "\n",
       "[10 rows x 31 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 下载 Kaggle Credit Card Fraud 数据集\n",
    "file = tf.keras.utils\n",
    "raw_df = pd.read_csv('https://storage.googleapis.com/download.tensorflow.org/data/creditcard.csv')\n",
    "raw_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>Time</th>\n",
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       "      <th>count</th>\n",
       "      <td>284807.000000</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
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       "      <td>284807.000000</td>\n",
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       "      <th>mean</th>\n",
       "      <td>94813.859575</td>\n",
       "      <td>1.165980e-15</td>\n",
       "      <td>3.416908e-16</td>\n",
       "      <td>-1.373150e-15</td>\n",
       "      <td>2.086869e-15</td>\n",
       "      <td>9.604066e-16</td>\n",
       "      <td>1.687098e-15</td>\n",
       "      <td>-3.666453e-16</td>\n",
       "      <td>-1.220404e-16</td>\n",
       "      <td>88.349619</td>\n",
       "      <td>0.001727</td>\n",
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       "      <th>std</th>\n",
       "      <td>47488.145955</td>\n",
       "      <td>1.958696e+00</td>\n",
       "      <td>1.651309e+00</td>\n",
       "      <td>1.516255e+00</td>\n",
       "      <td>1.415869e+00</td>\n",
       "      <td>1.380247e+00</td>\n",
       "      <td>4.822270e-01</td>\n",
       "      <td>4.036325e-01</td>\n",
       "      <td>3.300833e-01</td>\n",
       "      <td>250.120109</td>\n",
       "      <td>0.041527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>-5.640751e+01</td>\n",
       "      <td>-7.271573e+01</td>\n",
       "      <td>-4.832559e+01</td>\n",
       "      <td>-5.683171e+00</td>\n",
       "      <td>-1.137433e+02</td>\n",
       "      <td>-2.604551e+00</td>\n",
       "      <td>-2.256568e+01</td>\n",
       "      <td>-1.543008e+01</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>54201.500000</td>\n",
       "      <td>-9.203734e-01</td>\n",
       "      <td>-5.985499e-01</td>\n",
       "      <td>-8.903648e-01</td>\n",
       "      <td>-8.486401e-01</td>\n",
       "      <td>-6.915971e-01</td>\n",
       "      <td>-3.269839e-01</td>\n",
       "      <td>-7.083953e-02</td>\n",
       "      <td>-5.295979e-02</td>\n",
       "      <td>5.600000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>84692.000000</td>\n",
       "      <td>1.810880e-02</td>\n",
       "      <td>6.548556e-02</td>\n",
       "      <td>1.798463e-01</td>\n",
       "      <td>-1.984653e-02</td>\n",
       "      <td>-5.433583e-02</td>\n",
       "      <td>-5.213911e-02</td>\n",
       "      <td>1.342146e-03</td>\n",
       "      <td>1.124383e-02</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>139320.500000</td>\n",
       "      <td>1.315642e+00</td>\n",
       "      <td>8.037239e-01</td>\n",
       "      <td>1.027196e+00</td>\n",
       "      <td>7.433413e-01</td>\n",
       "      <td>6.119264e-01</td>\n",
       "      <td>2.409522e-01</td>\n",
       "      <td>9.104512e-02</td>\n",
       "      <td>7.827995e-02</td>\n",
       "      <td>77.165000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>172792.000000</td>\n",
       "      <td>2.454930e+00</td>\n",
       "      <td>2.205773e+01</td>\n",
       "      <td>9.382558e+00</td>\n",
       "      <td>1.687534e+01</td>\n",
       "      <td>3.480167e+01</td>\n",
       "      <td>3.517346e+00</td>\n",
       "      <td>3.161220e+01</td>\n",
       "      <td>3.384781e+01</td>\n",
       "      <td>25691.160000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Time            V1            V2            V3            V4  \\\n",
       "count  284807.000000  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05   \n",
       "mean    94813.859575  1.165980e-15  3.416908e-16 -1.373150e-15  2.086869e-15   \n",
       "std     47488.145955  1.958696e+00  1.651309e+00  1.516255e+00  1.415869e+00   \n",
       "min         0.000000 -5.640751e+01 -7.271573e+01 -4.832559e+01 -5.683171e+00   \n",
       "25%     54201.500000 -9.203734e-01 -5.985499e-01 -8.903648e-01 -8.486401e-01   \n",
       "50%     84692.000000  1.810880e-02  6.548556e-02  1.798463e-01 -1.984653e-02   \n",
       "75%    139320.500000  1.315642e+00  8.037239e-01  1.027196e+00  7.433413e-01   \n",
       "max    172792.000000  2.454930e+00  2.205773e+01  9.382558e+00  1.687534e+01   \n",
       "\n",
       "                 V5           V26           V27           V28         Amount  \\\n",
       "count  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05  284807.000000   \n",
       "mean   9.604066e-16  1.687098e-15 -3.666453e-16 -1.220404e-16      88.349619   \n",
       "std    1.380247e+00  4.822270e-01  4.036325e-01  3.300833e-01     250.120109   \n",
       "min   -1.137433e+02 -2.604551e+00 -2.256568e+01 -1.543008e+01       0.000000   \n",
       "25%   -6.915971e-01 -3.269839e-01 -7.083953e-02 -5.295979e-02       5.600000   \n",
       "50%   -5.433583e-02 -5.213911e-02  1.342146e-03  1.124383e-02      22.000000   \n",
       "75%    6.119264e-01  2.409522e-01  9.104512e-02  7.827995e-02      77.165000   \n",
       "max    3.480167e+01  3.517346e+00  3.161220e+01  3.384781e+01   25691.160000   \n",
       "\n",
       "               Class  \n",
       "count  284807.000000  \n",
       "mean        0.001727  \n",
       "std         0.041527  \n",
       "min         0.000000  \n",
       "25%         0.000000  \n",
       "50%         0.000000  \n",
       "75%         0.000000  \n",
       "max         1.000000  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 对数据属性进行描述\n",
    "raw_df[['Time', 'V1', 'V2', 'V3', 'V4', 'V5', 'V26', 'V27', 'V28', 'Amount', 'Class']].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Examples:\n",
      "    总计: 284807\n",
      "    欺诈交易数量: 492 (0.17% of total)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# 检查数据集的不平衡情况\n",
    "neg, pos = np.bincount(raw_df['Class']) # 同Class属性对数据进行分析\n",
    "total = neg + pos #这里我们将欺诈数量作为正样本pos\n",
    "print('Examples:\\n    总计: {}\\n    欺诈交易数量: {} ({:.2f}% of total)\\n'.format(\n",
    "    total, pos, 100 * pos / total))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 清理、拆分和归一化数据\n",
    "原始数据有一些问题。首先，Time 和 Amount 列变化太大，无法直接使用。删除 Time 列（因为不清楚其含义），并获取 Amount 列的日志以缩小其范围。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "cleaned_df = raw_df.copy()\n",
    "\n",
    "# 删除Time列\n",
    "cleaned_df.pop('Time')\n",
    "\n",
    "# 获取 Amount 列的日志以缩小其范围\n",
    "eps=0.001\n",
    "cleaned_df['Log Ammount'] = np.log(cleaned_df.pop('Amount')+eps)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "将数据集拆分为训练、验证和测试集。验证集在模型拟合期间使用，用于评估损失和任何指标，判断模型与数据的拟合程度。测试集在训练阶段完全不使用，仅在最后用于评估模型泛化到新数据的能力。这对于不平衡的数据集尤为重要，因为过拟合是缺乏训练数据造成的一个重大问题。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-1.42048887e+00  1.09880362e+00  1.63835847e+00 ... -1.72045796e-01\n",
      "  -1.39209006e-01  2.54952329e+00]\n",
      " [ 1.91977743e+00 -5.15399093e-01 -9.20753660e-01 ... -6.21946913e-02\n",
      "  -4.38767141e-02  4.19057856e+00]\n",
      " [-1.24128622e+00  1.10901088e+00  1.04311024e+00 ...  4.68305582e-01\n",
      "   1.96301942e-01  3.97031078e+00]\n",
      " ...\n",
      " [ 1.20486003e+00 -6.64091980e-03 -3.74094819e-01 ... -6.14565309e-03\n",
      "   1.49813518e-03  4.08934877e+00]\n",
      " [ 1.49671679e+00 -6.95956491e-01  1.85339914e-01 ...  1.64507476e-02\n",
      "   4.50199392e-03  1.79192612e+00]\n",
      " [ 1.11645056e+00 -8.78775630e-01 -1.44111798e+00 ...  1.35149531e-02\n",
      "   4.48778434e-02  5.05650692e+00]]\n"
     ]
    }
   ],
   "source": [
    "# 拆分为训练、验证和测试集\n",
    "train_df, test_df = train_test_split(cleaned_df, test_size=0.2)\n",
    "train_df, val_df = train_test_split(train_df, test_size=0.2)\n",
    "\n",
    "# 形成标签和特征的np数组\n",
    "train_labels = np.array(train_df.pop('Class'))\n",
    "bool_train_labels = train_labels != 0\n",
    "val_labels = np.array(val_df.pop('Class'))\n",
    "test_labels = np.array(test_df.pop('Class'))\n",
    "\n",
    "train_features = np.array(train_df)\n",
    "val_features = np.array(val_df)\n",
    "test_features = np.array(test_df)\n",
    "print(train_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training labels shape: (182276,)\n",
      "Validation labels shape: (45569,)\n",
      "Test labels shape: (56962,)\n",
      "Training features shape: (182276, 29)\n",
      "Validation features shape: (45569, 29)\n",
      "Test features shape: (56962, 29)\n"
     ]
    }
   ],
   "source": [
    "# 使用 sklearn StandardScaler 将输入特征归一化（平均值设置为 0，标准偏差设置为 1）\n",
    "scaler = StandardScaler()\n",
    "# 使用 train_features 进行拟合，以确保模型不会窥视验证集或测试集\n",
    "train_features = scaler.fit_transform(train_features) \n",
    "\n",
    "val_features = scaler.transform(val_features)\n",
    "test_features = scaler.transform(test_features)\n",
    "\n",
    "# np.clip为截取函数，截取大于-5小于5的数\n",
    "train_features = np.clip(train_features, -5, 5)\n",
    "val_features = np.clip(val_features, -5, 5)\n",
    "test_features = np.clip(test_features, -5, 5)\n",
    "\n",
    "\n",
    "print('Training labels shape:', train_labels.shape)\n",
    "print('Validation labels shape:', val_labels.shape)\n",
    "print('Test labels shape:', test_labels.shape)\n",
    "\n",
    "print('Training features shape:', train_features.shape)\n",
    "print('Validation features shape:', val_features.shape)\n",
    "print('Test features shape:', test_features.shape)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 查看数据分布\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pos_df = pd.DataFrame(train_features[ bool_train_labels], columns=train_df.columns)\n",
    "neg_df = pd.DataFrame(train_features[~bool_train_labels], columns=train_df.columns)\n",
    "\n",
    "sns.jointplot(x=pos_df['V5'], y=pos_df['V6'],\n",
    "              kind='hex', xlim=(-5,5), ylim=(-5,5))\n",
    "plt.suptitle(\"Positive distribution\")\n",
    "\n",
    "sns.jointplot(x=neg_df['V5'], y=neg_df['V6'],\n",
    "              kind='hex', xlim=(-5,5), ylim=(-5,5))\n",
    "_ = plt.suptitle(\"Negative distribution\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 定义模型和指标\n",
    "定义一个函数，该函数会创建一个简单的神经网络，其中包含一个密集连接的隐藏层、一个用于减少过拟合的随机失活层，以及一个返回欺诈交易概率的输出 Sigmoid 层："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 指标\n",
    "METRICS = [\n",
    "      keras.metrics.TruePositives(name='tp'), # 真正例\n",
    "      keras.metrics.FalsePositives(name='fp'),  # 假正例\n",
    "      keras.metrics.TrueNegatives(name='tn'), # 真负例\n",
    "      keras.metrics.FalseNegatives(name='fn'), # 假负例\n",
    "      keras.metrics.BinaryAccuracy(name='accuracy'), # 准确率是被正确分类的样本的百分比\n",
    "      keras.metrics.Precision(name='precision'), # 精确率是被正确分类的预测正例的百分比\n",
    "      keras.metrics.Recall(name='recall'),  # 召回率是被正确分类的实际正例的百分比\n",
    "      keras.metrics.AUC(name='auc'), # AUC 是指接收器操作特征曲线中的曲线下方面积 (ROC-AUC)。此指标等于分类器对随机正样本的排序高于随机负样本的概率。\n",
    "      keras.metrics.AUC(name='prc', curve='PR'), # AUPRC 是指精确率-召回率曲线下方面积。该指标计算不同概率阈值的精度率-召回率对。\n",
    "]\n",
    "\n",
    "def make_model(metrics=METRICS, output_bias=None):\n",
    "  if output_bias is not None:\n",
    "    output_bias = tf.keras.initializers.Constant(output_bias)\n",
    "  model = keras.Sequential([\n",
    "      keras.layers.Dense(\n",
    "          16, activation='relu',\n",
    "          input_shape=(train_features.shape[-1],)), # 隐藏层\n",
    "      keras.layers.Dropout(0.5), # 随机失活层\n",
    "      keras.layers.Dense(1, activation='sigmoid',\n",
    "                         bias_initializer=output_bias), # sigmoid层\n",
    "  ])\n",
    "\n",
    "  model.compile(\n",
    "      optimizer=keras.optimizers.Adam(learning_rate=1e-3), # 优化器\n",
    "      loss=keras.losses.BinaryCrossentropy(), # 损失\n",
    "      metrics=metrics) # 指标\n",
    "\n",
    "  return model"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 基线模型\n",
    "注：此模型无法很好地处理类不平衡问题。我们将在本教程的后面部分对此进行改进。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense (Dense)                (None, 16)                480       \n",
      "_________________________________________________________________\n",
      "dropout (Dropout)            (None, 16)                0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 1)                 17        \n",
      "=================================================================\n",
      "Total params: 497\n",
      "Trainable params: 497\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "EPOCHS = 100\n",
    "BATCH_SIZE = 2048\n",
    "\n",
    "# 早停机制\n",
    "early_stopping = tf.keras.callbacks.EarlyStopping(\n",
    "    monitor='val_auc', \n",
    "    verbose=1,\n",
    "    patience=10,\n",
    "    mode='max',\n",
    "    restore_best_weights=True)\n",
    "\n",
    "model = make_model()\n",
    "model.summary()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 训练模型\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "90/90 [==============================] - 2s 13ms/step - loss: 1.1795 - tp: 323.0000 - fp: 120843.0000 - tn: 106608.0000 - fn: 71.0000 - accuracy: 0.4693 - precision: 0.0027 - recall: 0.8198 - auc: 0.7785 - prc: 0.0194 - val_loss: 0.6323 - val_tp: 71.0000 - val_fp: 14543.0000 - val_tn: 30948.0000 - val_fn: 7.0000 - val_accuracy: 0.6807 - val_precision: 0.0049 - val_recall: 0.9103 - val_auc: 0.9164 - val_prc: 0.2200\n",
      "Epoch 2/100\n",
      "90/90 [==============================] - 1s 6ms/step - loss: 0.5103 - tp: 204.0000 - fp: 46936.0000 - tn: 135024.0000 - fn: 112.0000 - accuracy: 0.7419 - precision: 0.0043 - recall: 0.6456 - auc: 0.7448 - prc: 0.0607 - val_loss: 0.2624 - val_tp: 50.0000 - val_fp: 938.0000 - val_tn: 44553.0000 - val_fn: 28.0000 - val_accuracy: 0.9788 - val_precision: 0.0506 - val_recall: 0.6410 - val_auc: 0.8897 - val_prc: 0.3038\n",
      "Epoch 3/100\n",
      "90/90 [==============================] - 1s 6ms/step - loss: 0.2800 - tp: 160.0000 - fp: 16609.0000 - tn: 165351.0000 - fn: 156.0000 - accuracy: 0.9080 - precision: 0.0095 - recall: 0.5063 - auc: 0.7507 - prc: 0.1316 - val_loss: 0.1306 - val_tp: 42.0000 - val_fp: 356.0000 - val_tn: 45135.0000 - val_fn: 36.0000 - val_accuracy: 0.9914 - val_precision: 0.1055 - val_recall: 0.5385 - val_auc: 0.8890 - val_prc: 0.3768\n",
      "Epoch 4/100\n",
      "90/90 [==============================] - 1s 6ms/step - loss: 0.1853 - tp: 150.0000 - fp: 7119.0000 - tn: 174841.0000 - fn: 166.0000 - accuracy: 0.9600 - precision: 0.0206 - recall: 0.4747 - auc: 0.7876 - prc: 0.1604 - val_loss: 0.0745 - val_tp: 41.0000 - val_fp: 279.0000 - val_tn: 45212.0000 - val_fn: 37.0000 - val_accuracy: 0.9931 - val_precision: 0.1281 - val_recall: 0.5256 - val_auc: 0.8915 - val_prc: 0.4003\n",
      "Epoch 5/100\n",
      "90/90 [==============================] - 1s 6ms/step - loss: 0.1352 - tp: 155.0000 - fp: 3552.0000 - tn: 178408.0000 - fn: 161.0000 - accuracy: 0.9796 - precision: 0.0418 - recall: 0.4905 - auc: 0.8173 - prc: 0.2248 - val_loss: 0.0472 - val_tp: 43.0000 - val_fp: 219.0000 - val_tn: 45272.0000 - val_fn: 35.0000 - val_accuracy: 0.9944 - val_precision: 0.1641 - val_recall: 0.5513 - val_auc: 0.8994 - val_prc: 0.4756\n",
      "Epoch 6/100\n",
      "90/90 [==============================] - 1s 6ms/step - loss: 0.1067 - tp: 165.0000 - fp: 2041.0000 - tn: 179919.0000 - fn: 151.0000 - accuracy: 0.9880 - precision: 0.0748 - recall: 0.5222 - auc: 0.8576 - prc: 0.2986 - val_loss: 0.0322 - val_tp: 48.0000 - val_fp: 91.0000 - val_tn: 45400.0000 - val_fn: 30.0000 - val_accuracy: 0.9973 - val_precision: 0.3453 - val_recall: 0.6154 - val_auc: 0.9072 - val_prc: 0.5331\n",
      "Epoch 7/100\n",
      "90/90 [==============================] - 1s 6ms/step - loss: 0.0870 - tp: 173.0000 - fp: 1218.0000 - tn: 180742.0000 - fn: 143.0000 - accuracy: 0.9925 - precision: 0.1244 - recall: 0.5475 - auc: 0.8687 - prc: 0.3374 - val_loss: 0.0230 - val_tp: 50.0000 - val_fp: 21.0000 - val_tn: 45470.0000 - val_fn: 28.0000 - val_accuracy: 0.9989 - val_precision: 0.7042 - val_recall: 0.6410 - val_auc: 0.9041 - val_prc: 0.5803\n",
      "Epoch 8/100\n",
      "90/90 [==============================] - 1s 6ms/step - loss: 0.0734 - tp: 172.0000 - fp: 682.0000 - tn: 181278.0000 - fn: 144.0000 - accuracy: 0.9955 - precision: 0.2014 - recall: 0.5443 - auc: 0.8689 - prc: 0.3459 - val_loss: 0.0171 - val_tp: 51.0000 - val_fp: 12.0000 - val_tn: 45479.0000 - val_fn: 27.0000 - val_accuracy: 0.9991 - val_precision: 0.8095 - val_recall: 0.6538 - val_auc: 0.9082 - val_prc: 0.6144\n",
      "Epoch 9/100\n",
      "90/90 [==============================] - 1s 6ms/step - loss: 0.0633 - tp: 174.0000 - fp: 457.0000 - tn: 181503.0000 - fn: 142.0000 - accuracy: 0.9967 - precision: 0.2758 - recall: 0.5506 - auc: 0.8812 - prc: 0.3915 - val_loss: 0.0132 - val_tp: 52.0000 - val_fp: 10.0000 - val_tn: 45481.0000 - val_fn: 26.0000 - val_accuracy: 0.9992 - val_precision: 0.8387 - val_recall: 0.6667 - val_auc: 0.9084 - val_prc: 0.6445\n",
      "Epoch 10/100\n",
      "90/90 [==============================] - 1s 6ms/step - loss: 0.0545 - tp: 199.0000 - fp: 346.0000 - tn: 181614.0000 - fn: 117.0000 - accuracy: 0.9975 - precision: 0.3651 - recall: 0.6297 - auc: 0.8883 - prc: 0.4707 - val_loss: 0.0105 - val_tp: 53.0000 - val_fp: 10.0000 - val_tn: 45481.0000 - val_fn: 25.0000 - val_accuracy: 0.9992 - val_precision: 0.8413 - val_recall: 0.6795 - val_auc: 0.9098 - val_prc: 0.6638\n",
      "Epoch 11/100\n",
      "90/90 [==============================] - 1s 6ms/step - loss: 0.0478 - tp: 206.0000 - fp: 279.0000 - tn: 181681.0000 - fn: 110.0000 - accuracy: 0.9979 - precision: 0.4247 - recall: 0.6519 - auc: 0.9063 - prc: 0.5210 - val_loss: 0.0087 - val_tp: 55.0000 - val_fp: 10.0000 - val_tn: 45481.0000 - val_fn: 23.0000 - val_accuracy: 0.9993 - val_precision: 0.8462 - val_recall: 0.7051 - val_auc: 0.9082 - val_prc: 0.6754\n",
      "Restoring model weights from the end of the best epoch.\n",
      "Epoch 00011: early stopping\n"
     ]
    }
   ],
   "source": [
    "model = make_model()\n",
    "initial_weights = os.path.join(tempfile.mkdtemp(),'initial_weights')\n",
    "model.save_weights(initial_weights)\n",
    "model.load_weights(initial_weights)\n",
    "baseline_history = model.fit(\n",
    "    train_features,\n",
    "    train_labels,\n",
    "    batch_size=BATCH_SIZE,\n",
    "    epochs=EPOCHS,\n",
    "    callbacks = [early_stopping],\n",
    "    validation_data=(val_features, val_labels))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 查看训练历史记录\n",
    "针对训练集和验证集生成模型的准确率和损失绘图。检查是否过拟合。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_metrics(history):\n",
    "  metrics = ['loss', 'prc', 'precision', 'recall']\n",
    "  for n, metric in enumerate(metrics):\n",
    "    name = metric.replace(\"_\",\" \").capitalize()\n",
    "    plt.subplot(2,2,n+1)\n",
    "    plt.plot(history.epoch, history.history[metric], color=colors[0], label='Train')\n",
    "    plt.plot(history.epoch, history.history['val_'+metric],\n",
    "             color=colors[0], linestyle=\"--\", label='Val')\n",
    "    plt.xlabel('Epoch')\n",
    "    plt.ylabel(name)\n",
    "    if metric == 'loss':\n",
    "      plt.ylim([0, plt.ylim()[1]])\n",
    "    elif metric == 'auc':\n",
    "      plt.ylim([0.8,1])\n",
    "    else:\n",
    "      plt.ylim([0,1])\n",
    "\n",
    "    plt.legend();\n",
    "\n",
    "# 可视化\n",
    "plot_metrics(baseline_history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss :  0.6309481859207153\n",
      "tp :  85.0\n",
      "fp :  17927.0\n",
      "tn :  38937.0\n",
      "fn :  13.0\n",
      "accuracy :  0.6850531697273254\n",
      "precision :  0.004719076212495565\n",
      "recall :  0.8673469424247742\n",
      "auc :  0.8840129971504211\n",
      "prc :  0.15706372261047363\n",
      "\n",
      "Legitimate Transactions Detected (True Negatives):  38937\n",
      "Legitimate Transactions Incorrectly Detected (False Positives):  17927\n",
      "Fraudulent Transactions Missed (False Negatives):  13\n",
      "Fraudulent Transactions Detected (True Positives):  85\n",
      "Total Fraudulent Transactions:  98\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 评估指标\n",
    "# 使用混淆矩阵来汇总实际标签与预测标签，其中 X 轴是预测标签，Y 轴是实际标签\n",
    "train_predictions_baseline = model.predict(train_features, batch_size=BATCH_SIZE)\n",
    "test_predictions_baseline = model.predict(test_features, batch_size=BATCH_SIZE)\n",
    "\n",
    "def plot_cm(labels, predictions, p=0.5):\n",
    "  cm = confusion_matrix(labels, predictions > p)\n",
    "  plt.figure(figsize=(5,5))\n",
    "  sns.heatmap(cm, annot=True, fmt=\"d\")\n",
    "  plt.title('Confusion matrix @{:.2f}'.format(p))\n",
    "  plt.ylabel('Actual label')\n",
    "  plt.xlabel('Predicted label')\n",
    "\n",
    "  print('Legitimate Transactions Detected (True Negatives): ', cm[0][0])\n",
    "  print('Legitimate Transactions Incorrectly Detected (False Positives): ', cm[0][1])\n",
    "  print('Fraudulent Transactions Missed (False Negatives): ', cm[1][0])\n",
    "  print('Fraudulent Transactions Detected (True Positives): ', cm[1][1])\n",
    "  print('Total Fraudulent Transactions: ', np.sum(cm[1]))\n",
    "\n",
    "\n",
    "baseline_results = model.evaluate(test_features, test_labels,\n",
    "                                  batch_size=BATCH_SIZE, verbose=0)\n",
    "for name, value in zip(model.metrics_names, baseline_results):\n",
    "  print(name, ': ', value)\n",
    "print()\n",
    "\n",
    "plot_cm(test_labels, test_predictions_baseline)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "如果模型完美地预测了所有内容，则这是一个对角矩阵，其中偏离主对角线的值（表示不正确的预测）将为零。在这种情况下，矩阵会显示假正例相对较少，这意味着被错误标记的合法交易相对较少。但是，我们可能希望得到更少的假负例，即使这会增加假正例的数量。这种权衡可能更加可取，因为假负例允许进行欺诈交易，而假正例可能导致向客户发送电子邮件，要求他们验证自己的信用卡活动。"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 绘制ROC\n",
    "ROC绘图非常有用，因为它一目了然地显示了模型只需通过调整输出阈值就能达到的性能范围。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_roc(name, labels, predictions, **kwargs):\n",
    "  fp, tp, _ = sklearn.metrics.roc_curve(labels, predictions)\n",
    "\n",
    "  plt.plot(100*fp, 100*tp, label=name, linewidth=2, **kwargs)\n",
    "  plt.xlabel('False positives [%]')\n",
    "  plt.ylabel('True positives [%]')\n",
    "  plt.xlim([-0.5,20])\n",
    "  plt.ylim([80,100.5])\n",
    "  plt.grid(True)\n",
    "  ax = plt.gca()\n",
    "  ax.set_aspect('equal')\n",
    "\n",
    "plot_roc(\"Train Baseline\", train_labels, train_predictions_baseline, color=colors[0])\n",
    "plot_roc(\"Test Baseline\", test_labels, test_predictions_baseline, color=colors[0], linestyle='--')\n",
    "plt.legend(loc='lower right');"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 绘制 AUPRC\n",
    "内插精确率-召回率曲线的下方面积，通过为分类阈值的不同值绘制（召回率、精确率）点获得。根据计算方式，PR AUC 可能相当于模型的平均精确率。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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KyrR9+3b3/q5du7R27VrFx8ere/fumjNnjvbt26eXX35ZknTjjTfqqaee0u23366rr75aX3zxhd58803Nnz/fqD/CcTMZXQAAAIAPe+GbnXrhm13NHjcgLVovXHmSx9i1/1mpjfuanz24dkKmrp2Q1eYaGzN37lw9/vjjOu+88yRJmZmZ+umnn/Tcc8/pyiuvVHZ2tnr37q3x48fLZDKpR48e7nO7du0qSYqNjVVycnKz17r44otlsVhUU1Oj6upqnXXWWZozZ47788GDB2vw4MHu/QcffFDvvfeePvzwQ82aNUvZ2dmKiIjQWWedpaioKPXo0UNDhw6VVPt+/7x58/T555+7JyaysrK0dOlSPffccy0OME6nUy+99JJ75ujyyy/X4sWL9fDDD7fbNdqToSHphx9+0KmnnureP/JY3JVXXqmXXnpJubm5ys7Odn+emZmp+fPn69Zbb9Vf//pXdevWTS+88ALtvwEAAAJUaVWN8kqqmj0uJbbhI3oHy20tOre0qqbZY1qjvLxcO3bs0DXXXKPrrrvOPV5TU6OYmBhJ0syZMzV58mT17dtX06ZN01lnnaUpU6a06Xp/+ctfNGnSJDkcDm3fvl2zZ8/W5Zdfrtdff11S7cTE/fffr/nz5ys3N1c1NTWqrKx0/549efJk9ejRQ1lZWZo2bZqmTZumc889V+Hh4dq+fbsqKio0efJkj2vabDZ3kGqJjIwMd0CSpJSUFOXn50tSu12jPRkakk455RR3x4vGvPTSS42es2bNmg6sCgAAAL4iKjRIydHNv6PUJSK40bGWnBsV2r6/EpeVlUmSnn/+eY0aNcrjsyMd14YNG6Zdu3bpk08+0eeff64LL7xQkyZN0ttvv93q6yUnJ6tXr16SpL59+6q0tFQXX3yxHnroIfXq1Uu33XabFi1apD//+c/q1auXwsLCdP7557sbZERFRWn16tVasmSJFi5cqPvuu0/333+/Vq5c6f6zzJ8/X2lpaR7XbU33vKMfdTOZTO5H8drrGu3Jr95JCmRNR0UAAIDO69oJWW1+FO7ox++8JSkpSampqdq5c6cuvfTSJo+Ljo7WRRddpIsuukjnn3++pk2bpqKiIsXHx8tqtcrhcLTp+keCWGVlpSTp22+/1cyZM3XuuedKqg0lu3fv9jgnKChIkyZN0qRJkzR37lzFxsbqiy++0OTJkxUSEqLs7OwOe+ytf//+HX6N1iIkAQAAAO3sgQce0G9+8xvFxMRo2rRpqq6u1g8//KBDhw5p9uzZeuKJJ5SSkqKhQ4fKbDbrrbfeUnJysnsR1oyMDC1evFjjxo1TSEiI4uLimrzW4cOHlZeXJ6fTqW3btukPf/iD+vTpo379+kmSevfurXfffVczZsyQyWTSvffe67Fm0ccff6ydO3dq4sSJiouL04IFC+R0OtW3b19FRUXptttu06233iqn06nx48eruLhY3377raKjo3XllVce9z8rb1yjtQhJAAAAQDu79tprFR4erscee0y///3vFRERoYEDB+q3v/2tpNpg8Oijj2rbtm2yWCw66aSTtGDBAndnuMcff1yzZ8/W888/r7S0tAYzP/VdddVVkmofYUtOTtbEiRM1b948BQXV/qr/xBNP6Oqrr9bYsWOVkJCgO+64w6MddmxsrN59913df//9qqqqUu/evfW///1PJ554oqTaRg9du3bVI488op07dyo2NlbDhg3TXXfd1W7/vLxxjdYwuY71UlAAKikpUUxMjIqLi31inaR+932mGpdJJkm7/nimofXAt9ntdi1YsEDTp0+nRS+axf2C1uB+QWt01P1SVVWlXbt2KTMz02/WSULznE6nSkpKFB0d7ZXW7se6j1qTA/yvCX2A6frzzy7UajG2EAAAAACSCEkAAAAA4IGQZLSfV5N10d8OAAAA8AmEJIOZjC4AAAAAgAdCksFKatfwkr3GeewDAQAAAHgFIclgVT+vEebgaTsAAAB1ssbLaGftdf8QkgAAAGA4i6W206/NZjO4EvizI/fPkfuprVhM1mDOemG3pMqu6FDWpwAAAJ1PUFCQwsPDVVBQIKvV6pU1ddDxnE6nbDabqqqqOvxn6nQ6VVBQoPDwcPdCum1FSDJY/QnBartTYu00AADQCZlMJqWkpGjXrl3as2eP0eWgnbhcLlVWViosLEwmU8e3LDObzerevftxX4uQZLBgs1T9c88GnsEFAACdWXBwsHr37s0jdwHEbrfr66+/1sSJE2W1dvwTU8HBwe0yY0VI8iFEJAAA0NmZzWaFhvJoTaCwWCyqqalRaGioV0JSe+FhT4NVO+umAplIAgAAAIxHSDKY1VyXjJykJAAAAMBwhCSDWev9BIhIAAAAgPEISQaLrvdoJo0bAAAAAOMRkgyWGl4XjMhIAAAAgPEIST6EkAQAAAAYj5BksPrrXNG4AQAAADAeIclgVY667cKyauMKAQAAACCJkGS43aWm5g8CAAAA4DWEJINZ6mUkh5PH7QAAAACjEZIMVn8eycE7SQAAAIDhCEk+xOEgJAEAAABGIyQZrH53O2aSAAAAAOMRknwI3e0AAAAA4xGSDFZZrwV49sEK4woBAAAAIImQZLjY4LrtaofTuEIAAAAASCIkGa5LSN17SE5agAMAAACGIyQZrH4LcDISAAAAYDxCksH6xNQlo16JEQZWAgAAAEAiJBku2FJ/z9TUYQAAAAC8hJBksPqxaA/d7QAAAADDEZIMZqvX0C4hMrjpAwEAAAB4BSHJYLZ66yQdKKkyrhAAAAAAkghJhsuvrHvgjpAEAAAAGI+QZDBzvZeScooqjSsEAAAAgCRCkuHqvZKkuAjeSQIAAACMRkgyWIi5bp0kGoADAAAAxiMkGcxULxmVVtUYVwgAAAAASYQkw9WfPdp6oNSwOgAAAADUIiQZLDW87nE7p8t1jCMBAAAAeAMhyWBpEXXboVaLcYUAAAAAkERIMhzNGgAAAADfQkgyWEi9yaO02FDjCgEAAAAgiZBkuPohKSGKkAQAAAAYjZBkMHO95+1cNG4AAAAADEdI8iFOMhIAAABgOEKSweo3biittBtWBwAAAIBahCSDmeqlpFXZh4wrBAAAAIAkQpLhguqFJIuZhuAAAACA0QhJBguu192OkAQAAAAYj5AEAAAAAPUQkgxWv+t3jYP2dgAAAIDRCEk+pMbpUmkVHe4AAAAAIxGSfMyOgnKjSwAAAAA6NUKSwUwmyWqpa9hA6wYAAADAWIQkH2Cut1iSiZQEAAAAGIqQ5AOC6s0kxYUHG1gJAAAAAEKSDyivdri3K+2OYxwJAAAAoKMRknxAVkKEe7vSRkgCAAAAjERI8gEn90lwb9c4WSsJAAAAMBIhyQfYHU73dk5RhYGVAAAAACAk+YCicpt7+5ONuQZWAgAAAICQ5APqhyQLPcABAAAAQxGSfED3+HD3Nm8kAQAAAMYiJPmA+ovJEpIAAAAAYxGSfIDHE3YuYhIAAABgJEKSj9l9kO52AAAAgJEISb6g3kyS1cKPBAAAADASv5H7AKuZHwMAAADgK/jt3AdEhwa5t/ce4nE7AAAAwEiEJB8QVS8kHaqwK7+kysBqAAAAgM6NkOQDzh+W5rFfVGFr4kgAAAAAHY2Q5AOiw6w6f3g39/76vcUGVgMAAAB0boQkH1FcaXdvV9c4DawEAAAA6NwIST7i1L6JRpcAAAAAQIQkn/H4wi1GlwAAAABAhCSf9NG6/UaXAAAAAHRahCQfYTLVbVfZHcYVAgAAAHRyhCQfERxU96Ogux0AAABgHEKSj2I2CQAAADAGIclXuDx3bQ7agAMAAABGICT5iNKqmmPuAwAAAPAOQpKPiA6zeuyXEZIAAAAAQxCSfMT5w7t57LuOfv4OAAAAgFcQknxEl8hgRYcGufedvJIEAAAAGIKQ5COuGJOhswanuvdX7DpoYDUAAABA50VI8iHV9rrpo/3FVQZWAgAAAHRehCQfMn1gsnvbYjYZWAkAAADQeRGSfEiY1eLeJiIBAAAAxghq/hB4w9ur9urlZbvd+1sPlBlXDAAAANCJMZPkI3YVlmn93mL3/vb8UgOrAQAAADovQpKPcB21LFJKTJgxhQAAAACdHCHJR5l4KQkAAAAwBCHJRxw1kSTn0VNLAAAAALyCkOQjjs5E3+8sMqYQAAAAoJMjJPmoPomRRpcAAAAAdEqEJB8xoXeCx/7W/DI5nTxyBwAAAHgbIclHjOuVoJMy4jzGdhSwVhIAAADgbYQkH3LmwBRFhtSt71vDTBIAAADgdYQkHzJzXKZGZ3Vx72cXVRhYDQAAANA5EZJ8TFRo3UyS1cJiSQAAAIC3EZJ8TGpsqHvb6TSwEAAAAKCTIiT5EJfLpdKqGvc+C8oCAAAA3kdI8iHVNU6PRWRzi6sMrAYAAADonAhJPsTlkrYeKHXvx4ZbDawGAAAA6JwIST7k6MfrNueVNnEkAAAAgI5CSPIhR4ek+msmAQAAAPAOQpIPObqbncVMC3AAAADA2whJPqRBNzu62wEAAABeR0jyIUeHpFe+z5aLoAQAAAB4FSHJh0SFWj3eQ9p3uFIllTXHOAMAAABAeyMk+ZDgILNuOqWne3/6gGSFBVsMrAgAAADofAhJPiYlJtS97XC5FBzEjwgAAADwJn4D9zHRoXULyKbFhhtYCQAAANA5EZJ8THxksHvbJZo2AAAAAN5GSPIx9VdGyj5YoSq7w7BaAAAAgM6IkORjTKa6mLR4c75GPPS5vtteaGBFAAAAQOdCSPIxpqP2y6pr9NH6XENqAQAAADojQpIfYEFZAAAAwHsIST4mLjy4wZjFfPT8EgAAAICOQkjyMd27NGz7TUgCAAAAvIeQ5IMuGN7NY5+QBAAAAHgPIckPWEyEJAAAAMBbCEk+qH6bhnE9u8hiISQBAAAA3mJ4SHr66aeVkZGh0NBQjRo1SitWrDjm8U8++aT69u2rsLAwpaen69Zbb1VVVZWXqvWO+gvIjsiI11VjMw2sBgAAAOhcDA1Jb7zxhmbPnq25c+dq9erVGjx4sKZOnar8/PxGj3/ttdd05513au7cudq0aZP+9a9/6Y033tBdd93l5co71uT+Se5ts0lKjgk1sBoAAACgczE0JD3xxBO67rrrdNVVV6l///569tlnFR4erhdffLHR47/77juNGzdOl1xyiTIyMjRlyhRdfPHFzc4++RtzvXeQIkOtBlYCAAAAdD6GhSSbzaZVq1Zp0qRJdcWYzZo0aZKWLVvW6Dljx47VqlWr3KFo586dWrBggaZPn+6Vmr2lfkh64ZudBlYCAAAAdD5BRl24sLBQDodDSUlJHuNJSUnavHlzo+dccsklKiws1Pjx4+VyuVRTU6Mbb7zxmI/bVVdXq7q62r1fUlIiSbLb7bLb7e3wJ2m7I9c/uo6uEXU/lhqHU9sPFKtHfMP1k9C5NHW/AI3hfkFrcL+gNbhf0Bq+dL+0pgbDQlJbLFmyRPPmzdM//vEPjRo1Stu3b9ctt9yiBx98UPfee2+j5zzyyCN64IEHGowvXLhQ4eG+ETwWLVrkse9ySUd+NAVlNt332te6KMvp/cLgk46+X4Bj4X5Ba3C/oDW4X9AavnC/VFRUtPhYk8vlcjV/WPuz2WwKDw/X22+/rXPOOcc9fuWVV+rw4cP64IMPGpwzYcIEjR49Wo899ph77L///a+uv/56lZWVyWxu+PRgYzNJ6enpKiwsVHR0dPv+oVrJbrdr0aJFmjx5sqxWz3ePet+70L19zuAUPXb+QG+XBx9zrPsFOBr3C1qD+wWtwf2C1vCl+6WkpEQJCQkqLi5uNgcYNpMUHBys4cOHa/Hixe6Q5HQ6tXjxYs2aNavRcyoqKhoEIYvFIklqKuuFhIQoJCSkwbjVajX8B3VEY7VYTJLj5z+SSyafqRXG86V7F76P+wWtwf2C1uB+QWv4wv3Smusb2t1u9uzZev755/Wf//xHmzZt0k033aTy8nJdddVVkqQrrrhCc+bMcR8/Y8YMPfPMM3r99de1a9cuLVq0SPfee69mzJjhDkuB4pS+ie5tE2vJAgAAAF5j6DtJF110kQoKCnTfffcpLy9PQ4YM0aeffupu5pCdne0xc3TPPffIZDLpnnvu0b59+9S1a1fNmDFDDz/8sFF/hA5Tf2bMREoCAAAAvMbwxg2zZs1q8vG6JUuWeOwHBQVp7ty5mjt3rhcqM1b9hwcrbQ7D6gAAAAA6G0Mft0PT9h+ucm87jemtAQAAAHRKhCQfNahbjHt78aYDBlYCAAAAdC6EJB+VFF3Xkc/JRBIAAADgNYQkHzWmZ4J722KhcQMAAADgLYQkHzUiI04hQbU/Hgvd7QAAAACvIST5qJAgizK6hEuSzIQkAAAAwGsIST7syKtIZCQAAADAewhJPsxW45QkVdgcsjucBlcDAAAAdA6EJB+WX1rt3s4trjSwEgAAAKDzICT5sAqbw71dZWcmCQAAAPAGQpIPi48Idm/zuB0AAADgHYQkH1ZaZXdvH66wH+NIAAAAAO2FkOTDukSGuLfNdLgDAAAAvIKQ5CdoAw4AAAB4ByHJh7lcLvd29sEKAysBAAAAOg9Ckg9zOOtC0o/7SwysBAAAAOg8CEk+LDw4yL29fFeRgZUAAAAAnQchyYdZ6nVr2JxXamAlAAAAQOdBSPJhIzPijS4BAAAA6HQIST7sgbNPVI/4cPd+UbnNwGoAAACAzoGQ5MNCrRbtKarravePL7cbWA0AAADQORCSfNyvTkp3b7/8/R4DKwEAAAA6B0KSjwuysIosAAAA4E2EJB934Yi6mSR7jdPASgAAAIDOgZDk44KD6n5ELkk/sagsAAAA0KEIST7ObPJ83O67HYUGVQIAAAB0DoQkHxcREuSxX3+BWQAAAADtj5Dk49Jiwzz2gyz8yAAAAICOxG/cfsBS75E75pEAAACAjkVI8gMOl8u97ZLrGEcCAAAAOF6EJD8wKjPevT2+V4KBlQAAAACBj5DkB+q3Ae8SGWJgJQAAAEDgIyT5maIym9ElAAAAAAGNkOQHdhWWu7c35bKYLAAAANCRCEl+YNqJye7t+z740cBKAAAAgMBHSPIDaXF1ayWVVtkNrAQAAAAIfIQkP3DGgBT3dlWN08BKAAAAgMBHSPIDUaEWj/3iCmaTAAAAgI5CSPIDwUGeIamqxmFQJQAAAEDgIyT5AYfT5bFfUslMEgAAANBRCEl+wO7wfA+p3MZMEgAAANBRCEl+oMbhOZP0/Nc7DaoEAAAACHyEJD9Qc9TjdlV2ZpIAAACAjkJI8gM1Ts/H7RZvzjeoEgAAACDwEZL8QEyYVX/65UD3fv+UaAOrAQAAAAIbIckPhAcH6ZfDurn3f8otMbAaAAAAILARkvyU86j3lAAAAAC0D0KSn7Af1eHObDYZVAkAAAAQ2AhJfiKvpMpjf9/hSoMqAQAAAAIbIclPHL2g7OEKm0GVAAAAAIGNkOQnbDWeISnIzI8OAAAA6Aj8pu0njp5JsvBOEgAAANAhCEl+4ujGDQ/P/0lVdodB1QAAAACBi5Dkp77cUqDPfswzugwAAAAg4BCS/MTg9BilxYZ5jBWUVhtUDQAAABC4CEl+IiTIoofPHeAxFhzEjw8AAABob/yW7Uecrrr3knrEh+uXw7oZWA0AAAAQmAhJfiTUanFv7ymqUERIkIHVAAAAAIGJkORHRmbEu7cjgi3HOBIAAABAWzEV4UeCLGZldY3QzoJy1ThdevTTzR6fh1otOndomtLjww2qEAAAAPB/hCQ/U15dI0mqrnHqH0t2NPh84U95+vj/Jni7LAAAACBg8Lidn3G5XMf8fE9hhZcqAQAAAAITIcnPjO2Z4LF/1/QT9Np1o5QQGSxJCuVdJQAAAOC4EJL8zGMXDFb/lGj3/oDUGI/gFGrlRwoAAAAcD36j9jNWi1kT+3StGzDV/lelzSFJCg1iJgkAAAA4HjRu8EMu1b2XNPPfK7XpD9P03Z2nq9Lu8FhwFgAAAEDrEZL8UHx4sHvbVuPU+r2HNbR7nGJkNbAqAAAAIDDwuJ0fumBEuse+rcZpUCUAAABA4CEk+aH4iGDNGJzq3t9eUGZgNQAAAEBg4XE7PxUaVJdv7TVO/W3xNoVazeqXEq0Jvbse40wAAAAAx0JI8lM9uoS7t7/eVqAvNhdIki4Y3o2QBAAAABwHHrfzU5Ehdfk2NqyukUOolRbgAAAAwPEgJPmpzK6R7u3V2Yfc2ywmCwAAABwffqP2U6Z62xkJEe7tLQfK5HCyVhIAAADQVoQkPxUVWve43bqcw+7tr7cW6KqXVhpQEQAAABAYCEl+qv67R5n1ZpKk2qDkZDYJAAAAaBNCkp+KCbO6t1dnH9a/rhzh3g+2mGU2mxo7DQAAAEAzCEl+KqTeOklBZpNO75fknlEKC6bDHQAAANBWrJPkp7pEhri3a5wuVdkd+selw1RaVUPjBgAAAOA4EJL8WLDFLJvDKUla9NMBzRicanBFAAAAgP/jcTs/lhob6t4+WFZtYCUAAABA4CAk+bH/O623e9tCowYAAACgXfC4nR9zuOrePVqytUAmk0kJkSFKjQ3VoG6xxhUGAAAA+DFCkh+rvxbS4k35WrwpX5I0qV+iXrjyJKPKAgAAAPwaj9v5sV6JkY2O119oFgAAAEDrEJL82PAecfr3zJN09OtIYYQkAAAAoM0ISX7MZDLp1BMSdeXYDI9xFpMFAAAA2o6QFABGZ3Xx2GcmCQAAAGg7QlIACA7y/DE+9/VOgyoBAAAA/B8hKQDYapwNxorKbQZUAgAAAPg/QlIAqN8K/AiXq+EYAAAAgOYRkgLA0e8kSVJECEtgAQAAAG1BSAoAcRHBWnjrRI8x69F9wQEAAAC0CCEpQGR0ifDYL7M5DKoEAAAA8G+EpAARHGRWVte6oHTXuxsMrAYAAADwX4SkADIwLca9/cnGXE3/6zd65JNNNHEAAAAAWoGQFEDunt7Pve10ST/llui5r3bqvTX7DKwKAAAA8C+EpACSGB2qpOiQBuMLf8ozoBoAAADAPxGSAswTFw7R+F4Jig6tawH+6cYDBlYEAAAA+BdCUoAZ1ytB/712lAbUez9JkvYeqjCoIgAAAMC/EJIClNXi+aOtstMSHAAAAGgJQlKAqjxqnaQaJx3uAAAAgJYgJAWosuoaj/2iMptBlQAAAAD+hZAUoC4c0U2ZCXWLy17ywnJV1/DIHQAAANAcQlKAmjkuU+cNTfMYyymqNKgaAAAAwH8QkgLYjaf0VHC9Bg4hQfy4AQAAgObwW3MAs1rMOrlvV/d+iJUfNwAAANAcfmsOUJ9syNWcdzdoU26Je+ztVXsNrAgAAADwD4SkALVqzyH9b0W29h6qew/pua92GlgRAAAA4B8ISQGq3FbTYKy40q6HPv5JxZV2AyoCAAAA/AMhKUCVVNaFpKjQIPf2C0t36f01+4woCQAAAPALhKQAVX+2aESPOI/PdhWWe7scAAAAwG8QkgLUlBOT3NszBqdq3rkD3fsvfbfbgIoAAAAA/0BIClClVXWP20WEBGl8rwSPz7/eWqCcogpvlwUAAAD4PEJSgCqvrgtJkSFBSosL8/j8ihdX6LTHl3i0CAcAAABASApYR4cks0lKj/cMSnaHS+v3HvZyZQAAAIBvIyQFqLJqh3s7IiRIJpNJr1w9SrdN6aORGfHuz+54Z4NeWbbbgAoBAAAA3xTU/CHwRzPHZujkvl1VVlWj5JhQSVJGQoRmndZbJpNJK3YXuY/9aH2uLh+TYVClAAAAgG9hJilADewWo18MTtUlo7orMsQzC186qrsm96/rfrdiV5Eu/9dy5ZdUebtMAAAAwOcQkjqh2PBgPXj2AI+xb7YV6oO1+w2qCAAAAPAdhKROKik6RBcM7+Yxto4mDgAAAAAhKVB9tG6/HvjoRz2+cIvyihs+RmcymfTYBYP14Nknusc+Xp/rzRIBAAAAn0RIClDfbi/Uv7/drb9/sV2HKmxNHndSZrzHPi3BAQAA0NkRkgJUWb11kipsjiaPOyE52mP/3H98pz0HyzusLgAAAMDXEZICVP2Q9MtnvtPKei2/jzYwLca97XC6tLOAkAQAAIDOi5AUoNLjwj32l+042OSx/7l6pEKC6m6Fq15aqUPlTT+iBwAAAAQyQlKA+t2UPhrcrW6GKDzY0uSx8RHB+tVJ6R5jOwvLOqw2AAAAwJcRkgJUbHiwfjWyu3s/KjToGEdLt07u47F/9Us/qKTK3iG1AQAAAL6MkBTASuuFnMgQ6zGPjQ0P1tlDUt37xZV2rc0+3FGlAQAAAD7L8JD09NNPKyMjQ6GhoRo1apRWrFhxzOMPHz6sm2++WSkpKQoJCVGfPn20YMECL1XrX8qq6po3NDeTJEk3TOzpsf/8NzvlcrnavS4AAADAlzX/m3MHeuONNzR79mw9++yzGjVqlJ588klNnTpVW7ZsUWJiYoPjbTabJk+erMTERL399ttKS0vTnj17FBsb6/3i/cBlo3vo5L6JKquu0YB6Heya0j81WheO6KY3f9grSfpmW6F2FJSpV2JUR5cKAAAA+AxDQ9ITTzyh6667TldddZUk6dlnn9X8+fP14osv6s4772xw/IsvvqiioiJ99913slprHx/LyMjwZsl+JTE6VInRoa065xeD09whSZLyiqsJSQAAAOhUDHvczmazadWqVZo0aVJdMWazJk2apGXLljV6zocffqgxY8bo5ptvVlJSkgYMGKB58+bJ4Wh6sVS0zvjeCZrQO8G9/8SiLQZWAwAAAHifYTNJhYWFcjgcSkpK8hhPSkrS5s2bGz1n586d+uKLL3TppZdqwYIF2r59u37961/Lbrdr7ty5jZ5TXV2t6upq935JSYkkyW63y243tnvbket3ZB1fbCnQ6ytzFBVi1SUju2l4j7hmzxmQEqVvthVKqm3+YPQ/J9Tyxv2CwMH9gtbgfkFrcL+gNXzpfmlNDYY+btdaTqdTiYmJ+uc//ymLxaLhw4dr3759euyxx5oMSY888ogeeOCBBuMLFy5UeHh4I2d436JFizrsu7/Yb9KXe2rXSIqv3KsDCc03Ykislo7cGtvyy2mM4WM68n5B4OF+QWtwv6A1uF/QGr5wv1RUVLT4WMNCUkJCgiwWiw4cOOAxfuDAASUnJzd6TkpKiqxWqyyWuoVR+/Xrp7y8PNlsNgUHBzc4Z86cOZo9e7Z7v6SkROnp6ZoyZYqio6Pb6U/TNna7XYsWLdLkyZPd71i1ty2fb5f27JQkTRxzksejdE0pq67RA6u/kCSFWc2aPn16h9SG1vHG/YLAwf2C1uB+QWtwv6A1fOl+OfJEWUsYFpKCg4M1fPhwLV68WOecc46k2pmixYsXa9asWY2eM27cOL322mtyOp0ym2tfp9q6datSUlIaDUiSFBISopCQkAbjVqvV8B/UER1ZS4Xd6d6Oiwxt0XXi6h3TJTLEZ/45oZYv3bvwfdwvaA3uF7QG9wtawxful9Zc39B1kmbPnq3nn39e//nPf7Rp0ybddNNNKi8vd3e7u+KKKzRnzhz38TfddJOKiop0yy23aOvWrZo/f77mzZunm2++2ag/gs8rqbegbFRoy24Mp9Mls6l2Oy688fAJAAAABCpD30m66KKLVFBQoPvuu095eXkaMmSIPv30U3czh+zsbPeMkSSlp6frs88+06233qpBgwYpLS1Nt9xyi+644w6j/gg+r7TegrKTnvhK4cEWWcwmXTKqu+ac0a/xc6pr5Pz51aUN+4rlcrlkMpm8US4AAABgOMMbN8yaNavJx+uWLFnSYGzMmDH6/vvvO7iqwBEc5DlZ2CUyWGOyumjvocomw4/d4fTYLyq3qUtkw0cWAQAAgEBkeEhCx7pxYk8drrCpsNQmSTKZpItHdtfQ7k23Au8S4fmI3Tur9+r6iT07tE4AAADAVxCSAtzAbjF69drRrTrHZDKpX0q0NuXWdgCZt2CzzhiQovR432iZDgAAAHQkQxs3wHedMcCzDbur+eWVAAAAgIBASOpEXC6XHM6WpZ3fnN5bJyRHufdtR72nBAAAAAQqHrfrRA6UVGvMHxcrKiRIZw1O1bxzBx7z+M15pe7tLzfn60BJlbpEBuuEZGMX4QUAAAA6EiGpEzlcaZPLJZVU1WjptsJmj5/Yp6u+3logSXp4wSb3+J8vGKzzh3frsDoBAAAAI/G4XSdSZa97ZC6/tKrZ47s20fZ71Z6idqsJAAAA8DXMJHUi3eLC3NsjesQ3e/w9Z/ZTn6RIlVbVaOuBUi386YAk6X8rchQfEazfTz2hw2oFAAAAjMJMUidSaXO4t2PCrc0eHxcRrBtO7qnbpvbVuUPTPD57f83+dq8PAAAA8AWEpE6kuNLu3o4Jaz4k1Xdav0TNHJvh3o9u5fkAAACAvyAkdSL1Q1JsK0NOSJBFv53U273fNarx95UAAAAAf0dI6kQOV7R9JkmSCkqr3dtfby3QmuxD7VIXAAAA4EsISZ2Ix0xSC95JOlpRuc1j/zevrznumgAAAABfQ3e7TqS8ukaS9PgFg5XZNaLV5/dOilLXqBD3jFJOUaVeXb5HktQnKUonZTTfMQ8AAADwdYSkTmRsry6KCLYoNTZMw7rHtfr8+IhgLbntFJ049zP32N3vbXRvv3PTGA1vQWtxAAAAwJfxuF0ncmJqjFbdO1mD02NUUFotu8PZ/ElHCQ+2KC02rNHP9h9ufoFaAAAAwNcxk9TJhFot2nagVJP/8rUk6ZJR3TXv3IEtPt9kMumdm8Zq2c5C1ThcenvVXi3fVSRJCjKbOqRmAAAAwJsISZ3QoXpd7sKtllafnxwTqnOHdpMkzVuwyT1uJiQBAAAgAPC4XSd0qKKuS11cRPBxfVf9d5BW/DyjBAAAAPgzZpI6oUP1Wnk//81Od9c7STp/eDdldY1s8XdZLXWzR0u3FbZPgQAAAICBCEmdUP31kg5X2PWPJTvc+6OyurQqJJ0/vJs+2ZgnSdpyoFRVdodC2/AIHwAAAOAreNyuExrULVamdnp9KD0+3GN/0U8H2ueLAQAAAIMwk9QJjenZRUtuO0X7Dlc2+KxfcrQ255XoxldWqWtUiM4ekqbLRvdo8rt6dPEMSd/tOKgZg1PbvWYAAADAWwhJnVSPLhHq0SWi0c/W7T2s3QcrtPtghUZndTnm94QEWXTxyO7634psSdL/VmTr16f0bDDDBAAAAPgLHrdDAwWl1e7trlEhzR4/JD3GY//d1fv0yYZcrdxdJJfL1e71AQAAAB2JmSQ0UFBWF5ISWxCSLhyRrsc+26LCstqueX/5fKv7sz+eN1C/Gtm9/YsEAAAAOggzSWig/kxSWHDzOdpkMmlUZuOP5a3cfajd6gIAAAC8gZkkNFA/JD2+cItO7tO12XMePneAxvTsogpbjVbtOaTPfqztcvfO6r2yWkz64y8HdVi9AAAAQHsiJKGBtLgw97bD2bJ3imLDg91d8N5Yme0OSZK0eHN++xYIAAAAdCAet0MDV4zJcG8nR4e2+vyzh6Tp2vGZ7v2C0mo5Wxi2AAAAAKMRktDAoXKbe7sl3e2OFmq16OZTe3mM7S9uuCYTAAAA4It43A4NhAVbdO7QNOWXVql/anSbviM23Oqxf8c76xUZ4nm7xYUH65ZJvZUSEyYAAADAVxCS0EDPrpH6y0VDjus7TCaTMrqEa/fBCknSt9sPNnqcxWzSw+cOPK5rAQAAAO2JkIQmFVfYteVAqXu/e3y4kmNa/o7S5P5Jev6bXcc85tXl2eoWFy5JMpmkkZnxGtY9rm0FAwAAAO2AkIQmbdhXrMv+tdy9b7WY9MHN41v8CN7dZ/bXrFN7q7rG4TH+wdr9enjBJvf+nz7d7HGNb+84TYltaBgBAAAAtAdCElrM7nBpU25Jq95Tigm3SvJ8P2lI91iZTJKrkYZ3dodLFTZHww8AAAAALyEkoUlpcWG6YWKWlu08qPV7iyW1rdvd0U7KiNent0zU7oPl7rG739uowrLaRWzT48OP+xoAAABAW9ECHE3KTIjQnOn91Csx0j3WmneSjqVvcpSmnpjs/s+RgCTVNnMAAAAAjEJIQrMOlFS5t5M64F0hW43TY9/BwrMAAAAwECEJzcorrg1JYVaLokPb/wnNqqMaOxysN6sEAAAAeBshCc06UFIbWpJjQmUytf+jcNGhno0doo7aBwAAALyJkIRjKquuUVl1jSQp2UttuX/YU+SV6wAAAACNobsdjikyJEhbHpqm/JJq2R3O5k9oB3HhwV65DgAAANAYQhKaFRJk6fC23FkJEdpZWNsSvHsXWoADAADAOIQk+IQjAUmSPlq3X0Fmk4KDzJrYu6u6RB7/2kwAAABASxGS0KxVew4pMiRIyTGhiglr/6YKLpdLoVazquy1j/Pd/d5G92fDusfq3V+Pa/drAgAAAE0hJKFZN/53lQpKq5UcHarv7zq93b/fZDIpNSbMYzbpiNXZh7Ulr1ThwRZ1iwvrkO56AAAAQH0tDknr169v8ZcOGjSoTcXA99hqnCr8ed2ipJiO6273v+tH65tthapxOHWw3KbHPtvi/mzqk19Lkq4dn6l7zurfYTUAAAAAUitC0pAhQ2QymeRyuRr9/MhnJpNJDoej0WPgf/JLq3TkR74u57Ce/WqHbjy5Z7tfJyk6VOcP7yZJ2ne40iMkHbFyN63BAQAA0PFaHJJ27drVkXXARx15T+iIl77d3SEhqb602DA9fckwLd1eoAMl1fpic74kad3eYk189Ev949JhGpAW06E1AAAAoPNqcUjq0aNHR9YBH5WVEKFrx2fqhaW1ITk6zDuvsZ05KEVnDkrRN9sK3CFJkrKLKvTRuv2EJAAAAHSYFv/G++GHH7b4S3/xi1+0qRj4HrPZpJtO6ekOSSkxYV69/sjMeF04opve/GGve6xLJIvNAgAAoOO0OCSdc845LTqOd5ICT25xlXs7pQObNzQmJMiiR88frAMl1fpqa0GDegAAAID2Zm7pgU6ns0X/ISAFHs+Q5N2ZpCNKq+zu7WU7Dupvi7cpp6jCkFoAAAAQ2FgnCc3qEhmsc4akan9xlXonRRpSQ1x43SN2m/NKtTmvVN9uL9QbN4wxpB4AAAAErjaHpPLycn311VfKzs6WzWbz+Ow3v/nNcRcG3zGse5yGdY8ztIZ+KdFaXK+BgyQt31UkW41TwUEtnhAFAAAAmtWmkLRmzRpNnz5dFRUVKi8vV3x8vAoLCxUeHq7ExERCEtrd7Ml9NG1AslZnH9J9H/zoHr//ox8179yBBlYGAACAQNOmv4K/9dZbNWPGDB06dEhhYWH6/vvvtWfPHg0fPlx//vOf27tG+ACn06WLnlum3/xvjf79rffXzDKbTRqQFqPxvRI8xncXlnu9FgAAAAS2NoWktWvX6ne/+53MZrMsFouqq6uVnp6uRx99VHfddVd71wgfUFhWreW7ivThuv1auq3QsDqyukbq5lPrFrPNpnkDAAAA2lmbQpLVapXZXHtqYmKisrOzJUkxMTHKyclpv+rgM/YdrnRvp8Ya0+HuCIvJ5N5Ojws3sBIAAAAEojaFpKFDh2rlypWSpJNPPln33XefXn31Vf32t7/VgAED2rVA+Ib6bcCNDkldo+vWalq286DmvLtBDqfLwIoAAAAQSNoUkubNm6eUlBRJ0sMPP6y4uDjddNNNKigo0HPPPdeuBcI37PeYSfLugrJHKyjxXEz2fyuyWTMJAAAA7aZN3e1GjBjh3k5MTNSnn37abgXBN9V/3G5dTrHOHpJmWC3nDE3Tkq0FWr+32D12/0c/KjzY0uDYcb0SdOmoHt4sDwAAAH6uTSFp165dqqmpUe/evT3Gt23bJqvVqoyMjPaoDT6ksKxuLawXv92l26f1Vai1YSjxhqyukfpw1nj1vecTVdc4JUlLthQ0euyCDXkak9VFWV2NWQQXAAAA/qdNj9vNnDlT3333XYPx5cuXa+bMmcdbE3zQqX27urdNJslcr3mCUU7tm9ii4yJC2rxmMgAAADqhNi8mO27cuAbjo0eP1qxZs467KPie84Z1013vbVCV3anEqBAFB7UpX7erZy8froLSatU4nQ0+G/PIF+7tpGhj36ECAACAf2lTSDKZTCotLW0wXlxcLIfDcdxFwfe4XC7dNqWv9h6qVIjV+IB0RNeokAZjJVV29/YJyVHeLAcAAAABoE0haeLEiXrkkUf0v//9TxZL7XspDodDjzzyiMaPH9+uBcI3mEwmXTshy+gyWmR9Tl1Dh815DcM8AAAAcCxtCkl/+tOfNHHiRPXt21cTJkyQJH3zzTcqKSnRF1980czZQMfKOVTXDnxgWozsjtrH8Swmk8xm49+lAgAAgG9r03NT/fv31/r163XhhRcqPz9fpaWluuKKK7R582YWkw1gthqn/v3tLlXX+PYjlSH13pfasK9Yve/+RL3v/kSDH1ioz386YGBlAAAA8AdtbvuVmpqqefPmtWct8GEVtho5XdIDH/2kKrtTN53S0+iSmnSwXrvy+kqra7RgY64m9U/yckUAAADwJ21+A/+bb77RZZddprFjx2rfvn2SpFdeeUVLly5tt+LgO1bsKtJjn26WJJXWa4zgiy4cka5zh6ZpZEa8RmbEKy02zP3ZvkOVxzgTAAAAaGNIeueddzR16lSFhYVp9erVqq6ullTb3Y7ZpcC0/3CV/rNsjyQpLS6smaONFRNu1V8uGqI3bxyjN28cozMHpbg/W76ryMDKAAAA4A/aFJIeeughPfvss3r++edltVrd4+PGjdPq1avbrTj4jn2H65oh1J+Z8Qd9kuragNMSHAAAAM1pU0jasmWLJk6c2GA8JiZGhw8fPt6a4IPqP6bWzcdnko5Wv6EdLcEBAADQnDaFpOTkZG3fvr3B+NKlS5WV5R9r6aB19h2uC0mpfjaTFGSpu80HpsUYWAkAAAD8QZtC0nXXXadbbrlFy5cvl8lk0v79+/Xqq6/qd7/7nW666ab2rhE+oP5M0sfrcnW4ovEOcr6o0lbj3t6wr1gul8vAagAAAODr2tQC/M4775TT6dTpp5+uiooKTZw4USEhIfr973+va6+9tr1rhMEcTpfySqrc+7e/s14Du01QbHiwgVW1XHxEiMe+0yVZWFMWAAAATWjTTJLJZNLdd9+toqIibdy4Ud9//70KCgoUExOjzMzM9q4RBjObpJQY/3rErr7B6Z6P2P1l0VaDKgEAAIA/aNVMUnV1te6//34tWrTIPXN0zjnn6N///rfOPfdcWSwW3XrrrR1VKwxiMpn01o1jtHRboWqctY+qJUeHatWeQ0qJCVVydKjMZt+dmqm2Oz32f9hDG3AAAAA0rVUh6b777tNzzz2nSZMm6bvvvtMFF1ygq666St9//70ef/xxXXDBBbJYLB1VKwyUGhumC09Kd+/bapy64Nnv5HRJQ9Jj9f7N4wys7tjS48M1Y3CqPlq3X5L0/c4iTXj0C/35/MEaldXF4OoAAADga1r1uN1bb72ll19+WW+//bYWLlwoh8OhmpoarVu3Tr/61a8ISJ3IvsOV+nlSyS/WTRrRI85jP6eoUu+t2WdQNQAAAPBlrZpJ2rt3r4YPHy5JGjBggEJCQnTrrbfKZPLdR63QMbKL6haX7d4l3MBKWuasQSn6amuB1mQf0qEKuyTp9ZU52nuoUqFWi244OUsnZcQbXCUAAAB8QatmkhwOh4KD6zqaBQUFKTIyst2Lgu+rH5K6RPh+l7sukSF6ceZJmj4wxWN86fZCfb7pgB76+CeDKgMAAICvadVMksvl0syZMxUSUttSuaqqSjfeeKMiIiI8jnv33Xfbr0L4pOyD5e7th+Zv0mWjeyjU6vuPW07ql6T31+xTuc3hMe4PtQMAAMA7WhWSrrzySo/9yy67rF2Lgf8ID/a8dQ5V2PyiTfipJyRq7dwpstU49dJ3u/XYZ1skSWuyDxtbGAAAAHxGq0LSv//9746qA37mxpN76q+Lt0mSrBaTEqNCDa6o5awWs6wWsw6V29xjXaNC9PlPBxQebNHwjDiFBDGzBAAA0Fm1KiQBR4RazQoPtqjC5lC3uHBZfHidpKYc6c4n1Xbru/blHyRJ5wxJ1ZO/GmpQVQAAADAaIQltcqjC7l5Ytnu873e3a0yItfG+JWtyDquwrLrBuNVsVky4taPLAgAAgMEISWiT+Ihgbf7DNOWVVMlW4zS6nDb5zWm9lR4XrkMVNq3NOaxFPx2QJO05WKERD33e6DlnD0nVX5llAgAACGiEJLSZ2WxSSkyoCsqqlV9a5R4Ps1oUFer7My5hwRZdMqq7JOmVZbvdIelYPm/BMQAAAPBvhCQcF6dLGvnwYo8xk0m658z+umZ8pkFVtd75w9O1o6Bcew9VNPisqNym1T93vyu3OTT+T18ozGrRbyf10ZmDUhocDwAAAP9GSEK7c7mkLzYf8KuQFBZs0f2/OLHRzz7dmKcb/7vKvb/3UKUk6R9LthOSAAAAAhAhCcdtSv8kSbVrJa3cfUiSlOoHaya11NheXTShd4I25ZaosKyubXhEMP/zAQAACET8lofjYjGb9M8rRkiSPli7zx2SsrpGGllWu4oOteqVa0ZJkuYt2KR/fr1TkrR+32EDqwIAAEBHabwHMtAGOwvK3dtZXSMMrKTjVNhq3NsDUmMMrAQAAAAdhZCEdrOjoMy93TNAQ1L9duc/7DmknKKGjR4AAADg3whJaDcHSmrbgFvMJnWPD8yQlBwd6rF/pIkDAAAAAgchCe3mzRvGaOXdk/TWjWMUHGTWve9v1Kl/XqLNeSVGl9ZurpuY5bF/6Qvfa9WeIoOqAQAAQEcgJKHdmEwmdY0K0bDucfpxf7G255dpV2G55q/PNbq0duOSFGyp+5+N0yVtzy9r+gQAAAD4HUISOsTq7MNatvOgJCnpqEfU/Fl0qFUvXXWSx9hDH29ScYXdoIoAAADQ3ghJ6BDbD5S6t3snBk47cEka2ytBKTF1wa+0ukYb9hUbWBEAAADaEyEJHWJbvUfQeidFGVhJx5g7o7/H/iOfbNL6vYeNKQYAAADtipCEDnEkJHWJCFZ8RLDB1bS/iX26euz/uL9E//hyh0HVAAAAoD0RktDuDlfYVFBaLUnqFWCP2h0RZrXol8O6eYx1iwszqBoAAAC0J0IS2l39bm/LdxVpbc5hj89/2F2k57/e6eWq2pfJZNLjFw7W4PRY95jN4Wz6BAAAAPgNQhLa3YGSao/9iuoa9/YPu4tUXGnXwws2qcru8HZp7W5dvQA4IiPeuEIAAADQbghJaHejs+I1qFuMgoPMCg4yy2QyuT/77Rtr9cnGPEWGBHmsN+SvukaFuLfXZB8ysBIAAAC0lyCjC0Dg6RIZog9njW8wXlxp195DlXp71V4N7xEns9nUyNn+5ci7V5ICYmYMAAAAzCTBizbnlri3+6UERlvwyf2T3NsrdzOTBAAAEAgISfCaTR4hKdrAStpPcaXdvR0ebDGwEgAAALQXQhK8ZlNuqXs7UELSsO5x7u31e4s17MFFeuyzzQZWBAAAgONFSILXbM6rm0l66ovt+mprgYHVtI+j30MqKrfpua/8u705AABAZ0dIgtfsKix3b3+xOV/ZB8uPcbR/uGRUd43MjFf9HhQ1Tpdmv7FWucWVxhUGAACANiMkwWtOOyHR6BLaXZ+kKL15wxg9ev5gj/F31+zTC9/sMqgqAAAAHA9agMNrnvzVUN1zVn/ZHU5JUkiQRW+szNaAtBj1SYqS1Y/XTTopI06JUSHKr9cSPCUm1MCKAAAA0Fb++1sp/FJCZIhSYsKUEhOmPQfLdcc7G3Tm35bq7vc2GF3acenRJULL5pwuq6XuubsRGfEGVgQAAIC2IiTBMBv3Fbu3T0yNMbCS9mE2SXaHy73fPT7cwGoAAADQVoQkGGZDvZA0IM3/W4LvKPBsRBEfEWxQJQAAADgevJMEw2zcV9cS/OP1uVq8KV9SbYOH+o+qFVfa9dxXO1r0nVePz1RCZEj7FtpCB8vq3kcKDuLvHwAAAPwVIQmGcDpd2pZft7jsv7/d7d7uEhniEZLKqmv0jyUtC0nnDUtTQmSIqmscqrI5FRNubbeam7OzXotzW43Ta9cFAABA++Kvu2EIs9mkPklRHfb9320/qKEPLtTZTy3Vop8OdNh16ttd6P/rPgEAAICZJBjo7RvHasO+YtU4PWddMrpEeOx3iQjWa9eNatF3psaGSZKWbi+U0yWt21ssh9PVzFntI71eowYetwMAAPBfhCQYJizYopGZzbfJDrVaNLZnQqu++9vthZJqO86NyerSpvpaa++hSvc2j9sBAAD4L0ISAk5hWbU259W+7zQwLcZr7yUdvXjsb19fc8zjQ4IsunhUdw1Jj+3AqgAAANBahCQEnO92HHRvj+3Vuhmo41G/u50kvb92f7PnrM4+pEWzT+6okgAAANAGvDiBgPPttkL39ngvhqRhPeJktZhadc62/DJ9uTlfLpd33psCAABA85hJQsBZtKmum11EiPdu8VP6JmrVvZN1qNx2zOMe/WyL5q/Pde9f9dJKPXf5cE09MbmjSwQAAEALEJIQcIrqhZQKW41Xrx0dalV06LHfgRqaHusRkiSpmkYPAAAAPsMnHrd7+umnlZGRodDQUI0aNUorVqxo0Xmvv/66TCaTzjnnnI4tEH7D5XIpzGqRVNuGe1j3OIMrauiqcZl65ZqRHmOjWtDlDwAAAN5heEh64403NHv2bM2dO1erV6/W4MGDNXXqVOXn5x/zvN27d+u2227ThAkTvFQp/IHLJT1y3kD9clg3TR+QrNCfA5MvsZhNOinDMxTZHcwkAQAA+ArDQ9ITTzyh6667TldddZX69++vZ599VuHh4XrxxRebPMfhcOjSSy/VAw88oKysLC9WC19nNpt0ztA0PX7hYD35q6FGl9Ok3OIqj/3t+WUGVQIAAICjGRqSbDabVq1apUmTJrnHzGazJk2apGXLljV53h/+8AclJibqmmuu8UaZQLvbcVQoOvodJQAAABjH0MYNhYWFcjgcSkpK8hhPSkrS5s2bGz1n6dKl+te//qW1a9e26BrV1dWqrq5bv6akpESSZLfbZbfb21Z4OzlyfaPrCDQb9hWryu5UmNWifilRsphb15bbG8Zkxnrsv7VqryqqazR3xgmKCw9u9BzuF7QG9wtag/sFrcH9gtbwpfulNTX4VXe70tJSXX755Xr++eeVkNCy9W8eeeQRPfDAAw3GFy5cqPDw8PYusU0WLVpkdAkB4WCVtK/CpKJq6b3dte8inZTg1GW9ffN9n6Qwiw5U1gW4+RvzZC3dp1NTj71mEvcLWoP7Ba3B/YLW4H5Ba/jC/VJRUdHiYw0NSQkJCbJYLDpw4IDH+IEDB5Sc3HDNmB07dmj37t2aMWOGe8zprP0FOCgoSFu2bFHPnj09zpkzZ45mz57t3i8pKVF6erqmTJmi6Ojo9vzjtJrdbteiRYs0efJkWa3HbhuN5m3YV6w/PLtc9SeOKoJjNH36GOOKOoa9Ubv09y93qMpeF+LWlkUp2tX4XwAkRlqVXLpF06dyv6B5/PsFrcH9gtbgfkFr+NL9cuSJspYwNCQFBwdr+PDhWrx4sbuNt9Pp1OLFizVr1qwGx59wwgnasGGDx9g999yj0tJS/fWvf1V6enqDc0JCQhQSEtJg3Gq1Gv6DOsKXavFnwzIS9PQlw/TGDzn6emuBJOmkjC4++8/25tP66IaTe6nvvZ/K4aydPdp9sEK7D2Y3ec4vM0w6m/sFrcC/X9Aa3C9oDe4XtIYv3C+tub7hj9vNnj1bV155pUaMGKGRI0fqySefVHl5ua666ipJ0hVXXKG0tDQ98sgjCg0N1YABAzzOj42NlaQG4+iczhyUou92FLr3T+7T1cBqmmcymdQnKUqbclv2NxtBhvejBAAACHyGh6SLLrpIBQUFuu+++5SXl6chQ4bo008/dTdzyM7OltnMb4ZoGZfLpa+31c4iWS0mjcry7UVaLWaTPpw1TptzS+VwNXwXyeF06pfP1HV63FPme00oAAAAAo3hIUmSZs2a1ejjdZK0ZMmSY5770ksvtX9B8Ft7DlYop6hSkjSiR7zCg33iFj8mq8Wsgd1iGv2susbhsV9U3ehhAAAAaEe+/xsk0Arf/DyLJEkT+tQ1QCgsq5azkZmao0WFWBUWbOmQ2toiJMiirIQI7SwslyRtLTbr4QWbdf/ZAw2uDAAAIHARkhBQvt5WqOE94rS7sFwTe9e9j3TW35Yqr6Sq2fP/9MuBuuik7h1ZYquFh3iGtpV7DhlUCQAAQOfAyz4IKIfKbUqODtU/rxih/iltb/G+fu9hLdtx0N11zkiPnDvIY//H/aUa9uAi3fL6Gp+oDwAAINAwk4SAMnfGiVq797CG94jzGD+5T1cdqrA1e35abO0Cw88s2aFPNuYpITJYb904VpkJER1Sb0sM7BajiX26utuaS1JRuU0frN2v/zutl3olRhlWGwAAQCAiJCGgDOwW02gThD+dP6iRoxtXYavRl1vyJUkul9Q9Przd6mur6ydk6WBZlTbnlsjhqutw94ePN2ly/yRdPrqHgdUBAAAEFh63A46yZEuBquxOSdLUAcmymI1vuz2+d4Lev2mMLsh0eox/vbVA976/UbnFlQZVBgAAEHgIScBRFmzIdW9PH5BiYCUNZUW7FB/RcLXo4kq7AdUAAAAEJkISUE+V3aEvNtc+ahcXbvW5xWiTwqRvf3+yfj+1r8f4r19dbVBFAAAAgYeQBNTz9dYCVdhqF3A9XGnX4AcW6vudBz2O+XJLvoY/uEivLc82okQFWcwNOvftLChXfmnzLc4BAADQPEISUM/2gjL3tsslVdgcctZrs11WXaPHPt2ig+U2/ff7PUaUKEk69YREje+V4DE28dEvlVNUYVBFAAAAgYPudkA9vxzWTSt2FSn3cN2sTFhw3WKuC3/M00+5JZKkYT1ivV2eh/Sjuu5V2Z3KKapQTHjtO0uhQRYFB/H3IAAAAK1FSALqSYoO1UtXjWzy8w/W7ndvnz0kzRslNenOaSfI7nDq7VV73WOXvLDcvR0RbNFffzVUk/onGVEeAACA3+KvmYEWKiyr1tLthZKktNgwDe8e18wZHSsm3KqT+3Rt8vNym0NLtuZ7sSIAAIDAwEwS0EILNuTK8fP7Sb8YkiqzD6yfNOXEJF09LtP9LpXL5dJ3Ow6665zcP9nI8gAAAPwSIQlooffX7HNvnz0k1cBK6oQEWXTfjP7u/ZW7i/TNttrZrhOSozSxd0JTpwIAAKAJhCSgBbIPVmh19mH3/gnJni24H57/k77dflCRoUG6bUpfjcw0Zn2lf36902P/d2+tc28PSY/V5aN7yGQyfgYMAADAlxGSgBZYk3PomJ+v31vs7nr31Jfb9XJm080fOkqV3aHPNx1w72/OK9XmvFL3/rur92lAWoyGGfwuFQAAgK+jcQPQAmN7JujE1GgFB5nVo0t4g8+X7ypyb5+QHOXN0tyCzCYNSY895jGf/ZinjfuKvVMQAACAn2ImCWiBrlEhmv+bCU1+3j8l2j2TdP7wbt4qy0OQxax3bhyrnEMV7sYNH6/P1ROLtrqPee6rnXpx6S598btTGqyzBAAAgFqEJOA4/bi/7lG7wd1i1CfJmJkkSTKbTerRJcK9PzAtpsExdofLmyUBAAD4HR63A45T/cVczx+RbmAlDZ16QqLeuWmszhyY4h47/YREZpEAAACOgZAEHAdbjVMfrN0vSQoOMusXg3yjNXh9Q9Jj3TNdkvTrU3sZWA0AAIDv43E74Dh8sfmAisptkqSpJyYrJtwqSdq4r1j/WLK92fNHZ3XRFWMyOrJEfbIxV7sKy937w3vQ3Q4AAOBYCEnAcfhqa+3CrSMz4nVBvYYNBaXVWrAhr9nzF2zI0+isLh36HtOaeus7SZLT6ZLZzFpJAAAATeFxO+A4jM6Kl9Vi0q9P7alxvRJafb7VYlJCZEgHVFYn1Or5P/Nr/rNSG/bSBhwAAKApzCQBx+HsIWk6vV+SgswmWerNzozp2UXL5px2zHMtZpNqHC7FRwR3aI1b6i0oK0lfbilQkMWs568Y0aHXBQAA8FeEJOA4RYY0/J9RqNWilJgwA6ppaMbgVH2/s0hl1TXusRNTow2sCAAAwLfxuB0Q4M4ekqZ1c6d4jM0cm2FMMQAAAH6AkAR40X++263vthfK5fLugq5Ltxe6t6NCghQb3rGP+AEAAPgzHrcDvORgWbUe/Pgn1ThrA9JNp/TU0T3mgswmzZ7St92v/dK3u9zblXZHu38/AABAICEkAV6y6KcD7oAkSc8s2dHgmOAgc4eEpC+3FLi3e3QJ195DFUqJCfNoNgEAAIBaPG4HeEn3+HCfuPaOgnKN/9OXOvmxL1VcYTesJgAAAF/FTBLgJWN7Jeib209VzqGKJo8pLLN1yLVLqxqGob2HKpVzqEIx4TEdck0AAAB/RUgCvCg9PlzpTcwoVdhqNOaRL/Tyd7t148k9Nal/Urtd96lLhuntVXu1Oa9Um3JLJEl9k6LUP4VW4AAAAEfjcTvAR7z1w14VV9r1w55DWrAxt12/e1yvBD1x4WAF1XsH6dbJvWXmnSQAAIAGCEmAD3A4XfrX0roOdNeOz2r3ayz86YA27CuWJPVPidaU/sntfg0AAIBAwON2gA9Y9FOesotq31VKiQnVxn3F2rivWOnx4RrTs4vHsR+uy5XD1fwMUNeoEE3onaAgi1lOp0t/WbTV/dnsyX2YRQIAAGgCIQnwAa+vzHFv5xZX6fZ31kuSzh6S2iAkzftkiw6WH7vBQ7e4MO09VKm7pp+g6yf21IKNudqcVypJCg+2aHNeibYcKHUfPyQ9VuN6JbTXHwcAAMCvEZIAHxAaZGnX7+udGKlHzx+kgz93y/vv93vcn1XYHPrzwq0NzvnstxPVNzmqXesAAADwR4QkwAc8esEgnTEwWeXVDo/xjC4NO+HdOa2P7M7mH5UbmBajqFCrJCkhMqTZ43/YU6TCsmr3vsVs0pD0WIVa2zfAAQAA+DpCEuADokOtOntIWouOPWdIqqxWa6u+/4kLh+iSkd1VYasLYS99t1tLtxe69+9+b2OD83onRurT306UhfeXAABAJ0JIAgJEbnGlNuWW6NS+iTKZPENNcJBZY4965+in3BKPkNSYbfllcrpcsoiQBAAAOg9CEhAgnvpiu15dnq0h6bH68wWD1Ssx8pjHXzshU3HhVh0oqfYYf+rL7R77o+Yt1oszT9KQ9Nj2LhkAAMAnEZKAAJBTVKE3f6jtkLftQKm6RAQ3e054cJAuH5PRYPzLLfn6cX+Je7+o3Kal2woISQAAoNNgMVkgADz95XbZHS5J0tXjMxXXgpDUlHvP6q/BRwUi1lQCAACdCSEJ8HN7DpbrrVV7JUlRoUG6dnzWcX3f6KwuunpchsfY+2v2Hdd3AgAA+BMetwP83N8Wb5fDWTuLdO34LMWEt67zXWMm9O7qsb/1QJl+/9Y6SVKI1awLR6RrULfY474OAACALyIkAX5sZ0GZ3ltTO4sUE2bVVeMz2uV7I0OCFGQ2qebn8CXJPVslSd9tP6gvbjulXa4FAADgawhJgB/76+JtOpJjrp+YpejQ459FkmoXkh3YLUZrsg83+vnOwnK9unxPk+eHB1t0er+kdqsHAADAmwhJgJ/adqBUH67bL0mKjwjWlWMz2u27LWaT3rlxrHYfLJfT5ZLLJU3+y9cexzS2+Gx9k/ol6oUrT2q3mgAAALyFkAT4qcWb8+X6eRbpholZigxp3/85m80mZXWtXWvJ7nAqNSZU+4urWnz+hn3F+nF/sSJDgtSjS0S71gYAANCRCEmAn7rx5J4andVF/1q6S5eP6dGh17JazHr/5nH6dkehahyuRo+psDk098Mf3fsHSqp15t+WSqp9FPCu6f06tEYAAID2QkgC/JTL5VKfpEj9/eKhXrleYnSozh3arcnP9x2u1P0f/eie3arvx/3FHVgZAABA+2KdJMBPLdlSoPF/+lIvfLNTVXaH0eUoLTZMz1w6XJeO6q5zh6Z5fHbWoFSDqgIAAGg9QhLgh5xOlx77bIuKym16aP4mLdlSYHRJkqRpA5L18LkDdfW4TI/xILPJoIoAAABaj5AE+KEFG3P1U26JJGlgWoymnphkcEWeKmw1HvtF5TaDKgEAAGg9QhLgZ2ocTj2xcKt7/7apfWUy+dZMzftr93vsP7FoqxzOxhs+AAAA+BpCEuBn3lubq52F5ZKkUZnxmtg7weCKGjp6ZmtIeqwsPHIHAAD8BCEJ8CMOp/T3L3e491dnH9LNr61ucNy0J79W1pz5Ounhz/X+mn3eLFGSlBob5hGK/nD2AK/XAAAA0FaEJMCPHLJJufUWdLU7XKq2OxsctzmvVE6XVFBarddWZHuzRDmdLs15d4P78brRWfHaWVCmTzbk6pMNudqeX+bVegAAAFqLdZIAP9IlRLp+Qoa+3nbQvR5RWlyYxzGuoxYqmtLfu00dPt90QKv2HHLvf7+zSN/vLPI45r1fj9XQ7nFerQsAAKClCEmAHzGZpN9P6aO7zrQ2ecziTfnu7e7x4bp8TA9vlOZmczSc2TpaaVVNs8cAAAAYhZAEBBC7w6l5n2xy7995xgkKCbJ4tYbpA1L0z8vN2n2w3D02b8Fm93Z4sEXje/leswkAAIAjCElAAPnfimztLKgNJyN6xOmMAcler8FsNmnKiZ7XrR+SfnVSd5npdAcAAHwYjRuAAFFcadeTn29z799zVn+fWD9pwYZcj/1Zp/UyqBIAAICWYSYJCBBv/ZCjonKbJOnsIakakh6rb7cX6uH5m455nsPpUojVrItOStelo9r//aVfv1rXojyra4TiI4Lb/RoAAADtiZAEBIirx2UqITJEf128Tb+f2leSVFpl10+5Jc2e2z8lWn/46Cf96qTu7bro644Cz3bfQ9PpaAcAAHwfIQkIEGazSecMTdMvBqe63/kxmUwKDmr+qdpLR3dXUlRouwYkSXpzZY7H/jur9+rGk7PUOymqXa8DAADQnghJQICp3xRh6onJ2vrQGYbVMrpnFz339U73fkyYVamxYcc4AwAAwHg0bgA6IZfLJYfT1fyBx+nUvoke+5eM6q6IEP5uBgAA+DZCEtAJfbIxTzP+vlSr9hzq0Otsz/d8JymzS4SW7TioZTsO6mBZdYdeGwAAoK34K12gkymrrtEfPvpJeSVV+uUz3+nj/xuvAWkxHXKth+f/5LF/+zvr3dvBFrM+u3WiMhMiOuTaAAAAbcVMEtDJ/G3xNuWVVEmSgoPMOjE1usOudfDnluSNsTmcyv+5DgAAAF/CTBLQibhcLv3nu93ufVuNs0MXnP37xUP1/pr9qqpxSJLeWFm3ltO4Xl10UkZ8h10bAACgrQhJQCdiMplUXeN074/t2aVDr9ejS4RumdRbkjR/fa6eKd8hSYoKDdJj5w/26MQHAADgKwhJQCeyq7BcwRazbI7aoHTvWf29ct2C0mrd/f4Gj7GrX1rZpu+qsDkUFxGs6yZk6qxBqe1RHgAAgAdCEtBJuFwu3fP+BndAumFilvqldNz7SPV9vbVAhyvs7v3Sqhptzitt8/dlF1Xo0U+3EJIAAECHICQBncSOgjL9sLu25XdabJj7MThvmNAnQQPTYrQtv+3BqMru9Ng/tW/X4y0LAACgUYQkoJPolRilRbeerHs/2KgrxvRQeLD3/uefGBWqj/5v/HF9x+srsnXnu3WP7N15Rr/jLQsAAKBRhCSgEymqsKl/arRW7Tl0zIVko8OsuvHknh5j763Zq20HyhRkMeuMAclee1RPqn1U8F9Ld3mMfbhuX4PjIkKCdNoJiV4NgAAAIPDwmwTQify4v1jPLNnR7HFpsWENQtL89bn6fFO+JOm15dlacdfpXutOt3xXkbbll3mM3fHOhkaPPXNgip6+dJg3ygIAAAGKxWQBtMiRgCRJ8RFWr7bvNrdiLaetB0rldLo6sBoAABDomEkCOpHTT0hS5nURzR4XEmTx2N92wLPhwh/OHtCudTVnZGa83vv1WG1ppCPex+tztXR7oXt/W36Zzn76W31w8zjWYQIAAG1CSAI6keSYUCXHhLbqHLvDqdlvrnPvXzM+U6OzOnYR2sYM7R6nod3jGoznlVR5hCRJ2pJXqqoaB+8mAQCANuE3CADH9PSX27VhX7EkqVdipH4/ta/BFXm6alymSqtqPBo72BxODX/wc1XXOOR0SVldI/S3Xw3VgLQYAysFAAD+gneSADRpw95iPfXFdkmSxWzS4xcMVqjV0sxZ3hUTZtUpjayZVGmvDUiStLOgXJ9szPVyZQAAwF8xkwSgSU8s2qKan5PGzaf0VJDFpDdX5jR5fERIkE49oavXH3MbldlFF47opnU5xSqrrlFUaJA2H/X+0hsrc7R6z2FJtQEqPNiiU/p21fUTezbyjQAAoDMjJAFo0t8uHqqHPt6kH3OLNeu03nruqx16fNHWY55jRAvu4CCzHj1/sMfY5Ce+8mgbXlhmU2HZQY9jvttxUFNPTFaPLs03swAAAJ0Hj9sBaFJUqFV/On+Q3rxhjIKDWvavixqns4Orapmzh6TKamm+u90Ha/crr7jKCxUBAAB/wUwSgGYdeXzu1BMS1SUyxOMzl1z697e7tf3nWZvbp53g9foaM+u03rp+Yk85XXVrJq3LOayL/vm9x3FPLNqqNdmH9O+rRnq7RAAA4KMISQBabEBaTKMd4i4d1UMb9xUrp6hCPbtGGlBZ446e/eoWH66QILOqazxnu5KiW9cWHQAABDYetwPQLgakxeiMgSlGl3FMabFhWn7X6eqT5Bnk5pzRz6CKAACAL2ImCUCb5BRVKD0+3OgyWi061KqtB+oaOoRazaqucSi/xOEeM5lMSogMlsnU/DtNAAAg8BCSALTadzsKdcnzy3VSRpxundRH1iCzTkyN9nrr77Z4edluj/0qu1Mj5y1ucNzYnl306rWjCEoAAHRCvv8bDQCf4nK5dMc76yVJK3cf0iUvLJckJUQGa8nvT1VkiG//a2VNzuEWHbdiV5FKq2sUHWrt2IIAAIDP8e3fZgD4nOoapw6X2xuMF5bZVF5d4/Mh6dZJfRQebNGBkmqPFuF7DlZ4LEBb43Rp2l++liTtL65SVkKEbjylpy4cke71mgEAgHf59m8zAHxOqNWiV64dpXdX79XLy/a4x2eOzfCLLnEZCRF65LxBDcZf+GanHpq/yWNsf731k3YWluupL7YTkgAA6ATobgeg1QZ3i9HBcpvH/pzpvrE+UltNG5CskZnxCraYFR0apKTokAbHZBdVaPnOgwZUBwAAvImZJABtMrx7nBb+mKdQq0VPXTJMIUEWo0s6Lt3iwvXmDWM8xm5+dbXmb8j1GPvdW+u09I7TvFkaAADwMkISgFYzmUy6enymhvWI06EKm1+2Am+Jcb0SGoSkvYcq9c+vd0iSTDJpYp+u6pscZUR5AACggxCSALRKpc2h0uraxg2psaFKjQ1VfmnduzsJESEymwOjbfYlo7rr2x2Fmr/eMyjNW7DZvf3Eoq36/q7TFRNGFzwAAAIFIQlAq3y4bp/ueGdDk59nJkToo/8b7/Nd7loqNebYzSjsDqdCgni9EwCAQBIYv8UA8Bm7Csu1q6BcA7vFGF1Ku5hzRj+dekKiDlfUzp59uHa/Pv0xz/15jdOlartToVb/ficLAADUISQBaJW02HBN6Z/k3ndJWvTTAff++F4JOjE12oDKOobZbNLYngnu/T0HKzxCkiQVVdgUE87jdgAABApCEoBWGd87QeN7J3iMbdhbrBv/u0qS9PeLhwbMO0mNGZkZ57E/c2yGMhMiDKoGAAB0BEISgOM2sFuMPvq/8TpYVq24iGCjy+lQ/1uR47H/y2HdDKoEAAB0FEISgHYRHxGs+AAPSLnFlXp71V6PsTveWS/TzxNnB0qq1T0+TDPHZeoXg1MNqBAAALQHQhKAVissq9ar32fr5lN7KsjSeTq7fbRuf4Oxn3JLPPYLy6qVW7yJkAQAgB8jJAFotdveWqclWwr0l8+3SpLCg+s6u1XXOOVwutSza4T++quhGpAWGF3uJOm0E5L01g97tS2/TJIU/HNAtDmcHsflFlfp7vfq2qTbHbXd784YkKIxPbt4r2AAANAmhCQArVJld+ibbYUeYxU2R4PjdhSU65ONuQEVknolRmrR7JMbjJ/z9Ldam3PYY+zV5dkNjntn1V6tuncy7cIBAPBxhCQArRJqtWjOGSfondX7VFxhU1Row9bXQRaTJvTuqrOHpBlQofcNTItpEJIaU25z6MN1+xUZEqRT+yYqLJiwBACALyIkAWi1aydk6doJWUaX4TMePGeArh6fqQpbjcf4795cp815pR5jt7+9XpI0pX+S/nnFCK/VCAAAWo6QBOC4bdxXrIiQoE69XlBjf/a+yVENQtIRWw+Uant+458dkRgdquhGZuoAAEDHIiQBOC65xZW6+qWVqq5x6tnLhtOYoJ4/njdIp/dLUkmlXfe8v9Hjs90HKzTpia+PeX6Y1aL/XjtKw3vEHfM4AADQvjpP714A7a7CVqNr//OD8kurVVxp19+/2CaXy2V0WT4jLNiiXwxO1fnDuymiDe8fVdod2nRUi3EAANDxmEkC0CZOp0u/fX2tftxf+0t8enyYnrpkmExHVlaFW6jVon9eMUIfr9+vsmqHQoMa//upPUUVWrGryL1vMknnDu0czS8AAPAlhCQAbfLoZ1u08KcDkqSokCC9eOVJio8INrgq3zWuV4LG9Uo45jF//XybR0i6ckyGIkL41zQAAN7G43YAWu2tH3L07Fc7JElmk/TUpcPUOynK4Kr8W2FZtV75frfH2OVjehhTDAAAnRwhCUCrLN95UHe9t8G9f/8vTtTJfboaWJH/yy2u1IXPLVNhmU2SlBAZrE9umaCeXSMNrgwAgM6J5zgAtNieg+W64b+rZHfUNme4ckwPXTEmw9ii/FxOUYUmPPqlez8lJlT/vXYUAQkAAAMxkwSgxZbtOKjDFXZJ0sQ+XXXvWf0Nrsj/PfjxTx77fzh7AAEJAACDMZMEoMV+NbK7YsKseuarHXrqkqEKsvD3LMejwlbjbn5xRH5plT7/ecxiMWlUZrzCg/lXNQAA3sT/8wJosee/3qk1OYeUGhOmO99Z3+RxY3om6PLRnk0HZr+xVlU1jmavcc34LI/FU3cUlOnxhVsaHNc1MkQ3nNxTqbFhrfgT+JbHPmv457r7Pc9FZ7MSIvT57JNlNtNaHQAAbyEkAWixNTmHtGBDXrPHRYVYG4wt/OmAyqprmj33zIGpHvuHK+xNXtPhcumhcwY2+52+6siji8eys7BceSVVCm5ibaWm1NjtKrVLB8uqFWR1enwWGRKkUGvrF7cFAKCzICQB8FvDusc1f5APu/8XJ2pYjzgdKrfJ8vNMUWOzS2P/+EUbrxCke374qsFoVGiQnrl0uMb3Pva6TQAAdFaEJAAt9vA5A1vUrCGskVmKz2efLJdczZ4bG+a5IO2AtGgtm3OaXl+Ro78u3uYev/nUnjp3aFoLqvZdMWFWj8cSbTVOPfXFdlXam38s8XiUVtVo2c5CQhIAAE0gJAFosbiI4OYPakJyTGibzgsJsiglJkwFZdXusRtP7qnbpvSVyRRY7+kEB5n15K+G6N3Ve5VfWq0ux/HP2+l0KT//gBITk7Qpr1S5xVUen3+yIU/nDu2mXol00gMA4GiEJAB+4aGzB8gkKTzYojumBV5AOmLqicmaemLycX+P3W7XggULNH36UD20YIv+s2yPx+c7C8v1/pp9um1q3+O+FgAAgYaQBMAvmM0mPXTOAEkK2IDUUX45vJveW7NPJVWejTMW/XRAe4oqPMZMkib1T9IvBns20AAAoDPxiUVOnn76aWVkZCg0NFSjRo3SihUrmjz2+eef14QJExQXF6e4uDhNmjTpmMcD8E8LNuTqx/3FHmMmk4mA1AaDusXqjAEpDca3HCjVR+v2e/znw3X7dcvra5RfWtXINwEA0DkYHpLeeOMNzZ49W3PnztXq1as1ePBgTZ06Vfn5+Y0ev2TJEl188cX68ssvtWzZMqWnp2vKlCnat2+flysH0FE+WLtPs15brUtfWK6N+4qbPwHNGpUVr+AWLv6blRCh+PC2vw8FAIC/M/xxuyeeeELXXXedrrrqKknSs88+q/nz5+vFF1/UnXfe2eD4V1991WP/hRde0DvvvKPFixfriiuu8ErNADrOu6v36ra31snpql1H6P01+zQgLcbosvzeecO66fR+SSqp9Fyb6Zmvdui15dnu/SHpsfrnFcMV1MJABQBAIDI0JNlsNq1atUpz5sxxj5nNZk2aNEnLli1r0XdUVFTIbrcrPj6+o8oE4CVv/ZCj299ZL9fPncIvGdVdd03vZ2xRASQmzKqYsNqFfqvsDl3zn5X6dvtB9+cD02L0+vWjWWgWANDpGRqSCgsL5XA4lJSU5DGelJSkzZs3t+g77rjjDqWmpmrSpEmNfl5dXa3q6rrWwSUlJZJqOz/Z7c2vdt+Rjlzf6DrgHwL9fnlr1V7d/cFP7oB02ah03XdmXzkcNXJ07LJBAam5++Whjzd5BCRJ2rCvWC8u3aHrxmd2eH3wLYH+7xe0L+4XtIYv3S+tqcHwx+2Oxx//+Ee9/vrrWrJkiUJDG1+D5ZFHHtEDDzzQYHzhwoUKDw/v6BJbZNGiRUaXAD8SiPfLdwdMemNn3ezFxGSnRph26ZNPdhlYVWBo6n75YbNZjb2WunTtFqWVbOrgquCrAvHfL+g43C9oDV+4XyoqKpo/6GeGhqSEhARZLBYdOHDAY/zAgQNKTj72OiF//vOf9cc//lGff/65Bg0a1ORxc+bM0ezZs937JSUl7mYP0dHRx/cHOE52u12LFi3S5MmTZbVaDa0Fvi9Q75dXl2frjWV1M8dXje2hOdP60MXuODV3vwwcU6HTnljqMdYlIlhPzByjLpEh3ioTPiJQ//2CjsH9gtbwpfvlyBNlLWFoSAoODtbw4cO1ePFinXPOOZIkp9OpxYsXa9asWU2e9+ijj+rhhx/WZ599phEjRhzzGiEhIQoJafh/+Far1fAf1BG+VAt8XyDdL1vySnX/x3UB6ewhqTohJVrvr6v7ixOTSZrQu6uSYxqfLcaxNXa/HK6wae5Hno80Xziimx48Z4BCgngfqTMLpH+/oONxv6A1fOF+ac31DX/cbvbs2bryyis1YsQIjRw5Uk8++aTKy8vd3e6uuOIKpaWl6ZFHHpEk/elPf9J9992n1157TRkZGcrLy5MkRUZGKjIy0rA/B4DW65scpftn9Nf9H/2kX5/SU6OyuujKFxuuexYdGqQVd0+ioUA72JRboutf+UE5RZWSpCCzSXNn9Ndlo3swewcAwM8MD0kXXXSRCgoKdN999ykvL09DhgzRp59+6m7mkJ2dLbO57rn5Z555RjabTeeff77H98ydO1f333+/N0sH0A5mjsvUwG6xGtY9Vt9sK2z0GJPJJOeRjg5os/nrc3XbW+tUaa/thJEQGaynLxmmUVldDK4MAADfYnhIkqRZs2Y1+XjdkiVLPPZ3797d8QUB8KrhPeIkSb2TInX7tL568vNtstU4JUlhVov+fvFQhQf7xL+u/NbjC7fo719s9xi7ckwGAQkAgEawWiAAn5ESE6blO4vcASk6NEj/vXaUJvbpanBl/m1tzuEGAUmSHl+0Vav2HDKgIgAAfBshCYBPufesfooLtyohMkRv3DDGPcuEtkuMClFUaMOZuIhgCw0xAABoBM+vAPApvRKj9PLVoxQVGqSMhAijywkIiVEhOndoml5etsdj/O2bxiotNsygqgAA8F2EJACG2nqgVJkJEbJa6ia2B3aLMbCiwFJQWq3Zb2/Q9zuL3GPTTkzWoxcMUnQorXsBAGgMj9sBMMxXWwt0ztPf6va318vppHtde9tRIp3zzPfugBRkNumeM/vpmcuGEZAAADgGZpIAGOK9NXv1+7fWq8bp0ntr9mlo91hdMSbD6LICgsvl0gtLd+upHy1yqlqSlBQdoqcvGaYRGfEGVwcAgO8jJAHwun9+vUPzFmx27087MVkXjkg3sKLAcsc76/XmD3sl1S0Oe7jCrqtfWqkukSH68wWDNLwHYQkAgKYQkgB4jdPp0rwFm/TC0l3usctGd9cDvxggi9l0jDPRUrYa588ByVN1jVPVNU6VVNXo7VX7CEkAABwD7yQB8ApbjVOz31zrEZBmT+6jB88mILWn4CCzbp/WV726RightOF7XvERwbp8dA8DKgMAwH8QkgB0uPLqGl3zn5V6f+1+SZLZJD1y3kD95vTeMpkISO3t16f00kszhys22DMkjcnqok9umaD+qdEGVQYAgH/gcTsAHe5Pn27WN9sK3ft/OHuATu+XqPzSqibPCbaYFRse7DFWVG5TjdPZ7PUiQ4IUHlz3rzeH06WD5dUNjjPJpITI4IALaku3Feqyfy1X/b8Hmz25j24+tRezdgAAtAAhCUCH+92Uvlq+s0i5xZXqmRipe97fqHve33jMc8b16qJXrx3tMXbJ899rc15ps9ebc8YJuuHknu79g2XVGjlvcaPHDkyL0Vs3jlGo1dKCP4nvq7I7dPVLKz3GhqTH6jen9zaoIgAA/A8hCUCHiwmz6j9Xj1RxpV3vrN6rNdmHjS7JbcO+Yh2usCs5JjBCUnZRhWwOz9m2MwemGFQNAAD+iZAEwCuSY0KVHBOq3omRmtI/qdnjT0hp+N7MmJ5d1D0+vNlzMxIiPPaDg8ya0j9Jew9V6qfcEo/Pfj+1r5JjQpv9Tn9QYavRpS8s9xgLs5p1zfhMgyoCAMA/EZIAeNUFI9J1QRvXRJo748Q2nRcbHqx/XjFCucWVOutvS3Ww3Kao0CD95cIhmtSCwOYvisptKij1fPdq5pgeMvMeEgAArUJ3OwCdRkpMmP5+8VCdmBqtj2aND6iAVGGr0cx/r2wwfuPJzCIBANBazCQBCFgFpdUKtZoVFWp1j43tlaCPZo0PuNmVvYcqtT2/zGNsUprTo8sfAABoGf7fE0BAWrWnSL9+dbWGpMfq2cuGe7T5DrSAVGV36J73GnYLXJpn0qS/LJWvdziPCrXqzjNO0LheCUaXAgCAJEISgADjcrn00ne7NW/BJtkdLn324wG99N1uXTUucB87+37nQa3YXdRgvMph0p6iCgMqar1/fr2TkAQA8BmEJAABo7jSrjveXq9Pf8xzj43OitdZg1INrKrjnZQRrwm9E9wL9saF1z5eaLPZFBwcfKxTva66xqkKm8NjLDjIrEtGdTeoIgAAGiIkAQgI6/ce1s2vrVZOUaV77PqJWbp9al8FWQK7R01ESJBeuWaUx5jdbteCBQs0ffqpslqtTZzpXZ9uzNW9H/zoEZJOyojTH385SD27RhpYGQAAnghJAPyay+XSf77brYd/frxOql289vELBgdU9zp/ll9Spfs++NFjhi8yJEh3nHGCLh3ZPeDeEQMA+D9CEgC/VV3j0K1vrNWCDXW/fA9Jj9VTlwxVt7jmF51Fx3K5XHrzhxw9PH+TSqpq3OOnn5CoB88ZoNTYMAOrAwCgaYQkAH4r2GKWuV7rtmvHZ+r2aScoOCiwH6/zB3sOlmvOuxv03Y6D7rEuEcGa+4sTNWNQike3QQAAfA0hCYDfMplMeuS8gco5VKmbT+mpKScmG11Sp2d3OPX8Nzv1t8XbVGV3usfPG5qme87qr/gI32okAQBAYwhJAPxGcaVd2/PLNLxHnHssKtSq9389lpkJH/DD7iLd9d4GbT1Qt6htWmyYHj53gE7pm2hgZQAAtA4hCYBf+GF3kW55fa3Kqmv0yS0TPN5nISAZ7z/f7dbcD39sML7vcKVm/ntlu11ncLcY/ffaUYoK9Y2OfQCAwMSD+wB8msPp0l8/36YLn1umfYcrVVxp130fNPxlHMb6YO0+r1xn3d5ird9b7JVrAQA6L2aSAPisfYcrdevra7Vid5F7bGRGvB44+0QDq0JjbpvaV09/uV3fbj+oQd1iPBpqtEVJlV07C8objM8YnKpRmfHH9d0AADSHkATAJy3YkKs731nvbh1tNkm3nN5Hs07rJQvr6vicsT0TNLZnwnF/j9Pp0tur9+pPn2z2GM9MiNDcGf15twkA4BWEJAA+pcJWowc//kn/W5HjHkuLDdNffzVEIzKYQQhkew6W6+THljQY//3Uvrp2QqZCgizeLwoA0CkRkgD4DJfLpStfXKGVuw+5x84alKKHzx2omDBe1A90d723odHxs4ekEpAAAF5FSALgM0wmk66bkKWVu1e5xxxOl+a8u77BsYfK7QoPtujMQSk6b1g3b5aJDpJXXNXo+Jeb8xV9jJBcbXdqUHqMTkiO7qjSAACdDCEJgE+ZcmKyLhnVXa8tz5YkfbIx75jHf7ElXxP7dFVCZIg3ykMH2ZRbot0HKxr97N4WdDM0m6SP/2+C+qcSlAAAx48W4AB8znUTslp03KjMeA1Nj1VUKH/f4+/sDqccTlebz3e6ar8DAID2wG8WAHxOt7gwLZtzmnvf6ZJe/X6P+qdGa3iPOPe41WJWfHiwzHS783uDusXqzRvGaMWugwoPDlJTP9KCsmo9/eWORj87kVkkAEA7ISQB8DlWi1kpMWGSpJyiCt3+9not23lQWQkRWnDLBIVaeYk/EI3MjNfIZtZAWp19qNGQZDZJdodL9HcAALQHQhIAn+R0uvTqimw9smCTKmwOSdLOwnJ9sTlf0wemGFwdfI3TJY370xfH9R1lVTWyOZyKDg3Szaf20g0n92yn6gAA/oaQBMDn5BRV6I531uu7HQfdY2mxYXr0/EEa1+v4FyyF/3pn1d4mPysqt7XLNUqqavTsVzsISQDQiRGSAPiUN3/I0e1ve7b8vmRUd901vZ8Oldv06KebW/Q9v53UR8FBdb1pvtlWoGX1QldT0uLCdOmoHpJq120ymXjfyZdcOqqHNueVatWeQwq2mJUSG3pc37eniY56dEsEgM6NkATAZ2zPL2sQkF65ZqQm9O4qqbZN9D+WNP7S/tFmndZLwfUaeK7YVdSic0f0iNM5Q9L0jyXbtSO/XM9ePrwVfwJ0tP6p0XrnprHt9n1n/u0b/bi/pMH4tvwy5RZXut+NAwB0LoQkAD4jOjRIUSFBKq2ukSQ9dv4gd0Dylh/2HNJpjy/RgZJqSbULmZ56QqJXa4D39OgS3mhIGtQtRvERwQZUBADwBYQkAD4jMTpUX99+qjbl1f7S2jcpyuPzPklReu26US36rpCj2pxdMDxdY3p2afL4jfuKNW9B7aN8RwKS1WLSrsJyndriPwH8xc6CMv118bYGixWHBJn120l9dM34TI/HNQEAnQshCYBPiYsI1tiejTdniAmzNvlZc7p3CVf3LuENxgvLqvXYp1v05qocj/HTT0jUPWf1V2ZCRJuuB9+UU1Shvy7epvfW7PNYvDYhMkS/PqWnLhnVnRbzAABCEoDOyVbj1MvLduuvn29zP94nSVldI3TfWf11Sl8esQsk+w9X6qkvt+vNlTmqqReO4sKtuvHknrpiTIbCgglHAIBahCQAndIPu4v00PxN7v2okCDdMqm3rhiTwWNWASS/pEr/WLJDry3Pls3hdI+HWs26ckyGZo7LUERIkOxOp+xVzmN8U+PCrBZZLdwvABBoCEkAOp012Ye091Cle99kkm48paeiQ616f80+93hSTKhO7uPZOOLTjbkqqaxRcwanx6pvct07VeXVNZq/PrdF9U09MVkx4Vb3/o6CMq3afajZ88KCLZoxONVjbNnOg8orsTd7bmbXCJ2UEe8x9u7qvapxuJo4o86Ynl2UHl/3KOP2/DJtzy/VKX0TDX10bcPeYv3qn8tU/vNixPVV2Z167uudeu7rncd1jejQIL048ySNOOqfHQDAvxGSAHQ6H6/P1b+W7nLvu1zSY59taXDchN4JDULSnxdu1fb8smavcc+Z/TxCUnGlXbe/s/4YZ9QZ2j3WIySt3FWkO9/d0Ox5KTGhDULSayv26tMfDzR77kUj0huEpPs++FFl1c0HwqcvGab0+HBtO1CqZ5bs0Ls/B83LRnfXQ+cMbPb8jvL9zoONBqT2VFJVo6XbCwlJABBgCEkAgOOyft9hfbhunz5rQRjzpgtHpGtnYbn2Ha5s/uBmrMs5rOLKxmfkXl62Rx+u23/c12iJhIgQ3f+LE9U/Ndor1wOAzoqQBKDTmT4wRT27RjZ7XEpMaIOx307q3aLH7YZ2j/XYjwmzat65LZtVSYzyvO6IjPgWnRveSOOBi0Z008Q+zTehyOrasIvffTP6N/m4nUsurdhVpA/W7tdzXzV8ZO28oWm658z+zV63I8WEW/XIee0zk/Wb/61pMggVldtUVG5rl+s0Z2dBuV75fk+7/bkAAI0jJAHodIb3iNPwHnFtOvesQanNH9SIiJAgXTKqe5vO7ZUYqV6JzYe6xozv1UVWq7X5Axtx4Yj0Rse/3JKvp77YrlV7PN+TSowK0fUTs3TxyO6KCAms/3u5cES61u09rD0HKyTVvovUUVySSqsaD+LRoUH65bC0Drs2AKBWYP2/GACgw72zaq9HQEqPD9ONJ/fUL4d1C9g1hsb3TtBXv+/YZYVdLpe+3JKvp7/c0SCAJkSG6LoJmbpkVHdFhbYt9AIAWo6QBABokq3GKbNJCqrX5vrXp/TSx+tz1TsxUjef2ktnDUrx+Bytt/DHPF3/yqpGP5sxOFWPnT8oYAMoAPgiQhIAoIEqu0OPfbZF/1q6SxeO6Kbzh3s+enf7tL66dGQPjy58ReW2FnX+M5vUoBvc7sJy5ZdWN3tuXLhVvZOiPMbW5RxWdU3zaxz16BKupOi6972q7A6t31vc7HmSNKhbjEdIOVBS5X707lhCgswanB7rMbb1QKkOV9Q1gfhpf7Hu/+inJr/jo3X7dfnoHhqZGa+Vu4vkar4ru3onRiouIti9X1xp15a80iaPr6mp0Y4SaeXuQxrds6vMZpP7s5yiCuUUVahXUmSD9+UAIFARkgAADdzy+hp3t7o3f9irN3/Y2+CYYd3jNDqri3t/5e4i3dDEbEh9IUFmbXnoDI+x57/ZqVeXZzd77uT+SXr+ihEeY7P+t1o5Rc13sHvwnAG6fHQP935ucZUufG5Zs+dJ0te/P1Xdu9StBfXpxjzN/fDHZs/L6BKuJUc9pvenTzZr8eb8Fl1Xql3HK8hSG1ouef572VuwdtULV4zQpP5J7v0f9xfrkueXN3NWkP7240ptfegMBZtNcrlc+mHPIV3wbO0/I4vZpPd/PU4Du8W0uHYA8FeEJABAAy2ZJYF3vHrNKA3r3rZGI23hcLr04br9+tc3O7Wu3kybw+lSbnElIQlAp0BIAgA08PSlw/Te6n1at/ew+qc0viZPakyYx35GlwjdMDGr2e+21HuU64gJvRMU2YKOeEc/aidJF4/sruKKxtcwqu/oP0dMmLVF9UpS1FHd7E5MjW7RubHhwQ3Gpg5IVs/ESOUUVWjDvmLtPeQ5C2ZSbUfDi05K17UTPK9x7YQsOZ3NzyT1qDfrJdX+rI5Vr8Pp1E9bd+r7ArNO+fOXOlDS8NHHe87sp8n1ZqcAIJARkgAADfTsGqnbpvZt1Tl9k6M0Z3q/Nl1v2oAUTRuQ0qZzf31KrzadFx8R3OZ6R2TEN3ivqiUqbDWyO5z6cnO+th31/lZ0aJAuHd1DV47JUHIja3RJ0h3TTmhTvRkJEcf8s36/PV+vLNslp0seAal/SrSunZCpswalKjiI5hwAOg9CEgAAHSz7YIVeXrZbb/6Qo5Kj1kDqHh+uq8dl6IIR6YatL9UvJUqWehN8p5+QqGsmZGpMVheZTA1n/gAg0BGSAADoIJU2h25/Z70+Wre/yWPiI4L10fpcfbQ+95jfdbjCph0F5RrcLUa3TOqt005o/aNvDqdLC3/MU3ZRhW44uad7PDw4SKekOBWd3EPXTujZ5sWLASBQEJIAAOggizcfOGZAkqS1OYdb9Z3r9hbroY83tSoklVXX6M2VOfr3d7uUU1SpYItZ5w3rpq5RIe5jpnZzafr0/rJaWawWAAhJAAB0kJGZ8erZNUI7Csrb9Xt3FpbrmSU7dMmo7ooJazrUbNxXrNdWZOvDtftVVl33mJ/N4dSH6/brmvGZ7VoXAAQKQhIAAB0kMSpUn88+WS1oSOfBVuPUgPs/k+MYJ/7p080qrrTrzjM8mzmUVtn1/+3deXhTVcI/8G/2NOm+t9CFUvZVWkA2Ea2AoMLMKIzjy6DjMo7gxuvMyDgOjBvqOP5QQRg30HdEEF5leKWKUEEQUNkFgQLdWLrvbdJmPb8/2qZJm7ZJIU3afj/Pw0Nyc86957aHkG/OueduO5GPT368iFNXqlvVmzowAg9M6YfJyeHuNYqIqBdhSCIiIvIgiUTisCiCK+QyCfqFa3GhxQp4LSVFaFtt+936QziUW+GwTaOUYc7oWNw3qR8GOllGnYiIHDEkERER+ZjcUh2uTwptMyTFh2qw/I6hGJsYCiGEwwp0d4zuYwtJI/sG4ddj43HH6FiX7kNFREQN+I5JRETkA+pNFqzbn4uPDuaioKq+1ethWiUWTUvGL66LRVaJDp/8eAmPfHwUGx+agNFxwbZyc0bH4nxRDealxmF4n6AuPAMiop6DIYmIiMjL6k0W3LHqO5wrant6XZnOiF1nivDJjxcdbkS78ceLDiEpUK3Ac3OGe7K5REQ9HkMSERGRl5XpjO0GpCYHssocngeq5QjRKj3VLCKiXoshiYiIyMv6BPvhtbtG4anNJ9yqV11vxpo9WVizJ8vp6xIAYf4qh221BjPqTRYnpWV4/ODXAACpBLhhQATW/26cQ4kZ/28vLlXoO2zXX2cPxW/Gx9ue55XpcOsb+2zP9cbm4yeEafD6vFFISQjtcL9ERF2FIYmIiMgH3JnSF9+dL8HW4+3ffNYdAkBprcHF0s2LP1gFcLG8dRiqM1kcAk5bLFarYzsE2qyXV6bHf47nMyQRkU9hSCIiIvIRCyYkIrtUh58uVwEA/BQy1Jks8FfJEeQnt61iV2+yoFJv6nB/EgkQFah22FZdZ0JNvRnt3bpJKZc6XOfUpF+4Fn4KWYfHDdI4TgFUyKUY1Lj0eGZRTas2Lrg+ocN9EhF1JYYkIiIiH5GSEIJtiyd7ZN+ltQZsO56Pz45ddnqTWY1M4Bcp8bgzNQ6j44IdlhVv8mGL6XdtqdQb8T8Hc7Hl6BW89IvhGBYbhB1P3gAAKKkx4NY39mLWiBj8akxfjOwb5PRYRETexJBERETUg/2QXYZ39mZjz7kSWKzOx4+Gxwaij7QSUYEqHMgqa7VAhCssVoGcUh1+zq9CVrEOFtFwrL/952fcNDjSoezCiYmQSiT47kIpvrtQ6vIxIgJUuGNULNQujGYREV0NhiQiIqIeqqi6Hr9574c2w1GTU/nVOAUpdly+cM3bcCSvAkfyKq7Z/oqq6vHozQOu2f6IiJyRersBRERE5BlKmRQaZc8adQkPUHVciIjoKnEkiYiIqIcK0Sqx+6kbceJSJUQ7g0lmixlHDh9BSmoK5DLXPhqcuFyJt75xPvLUJ9gPUrvLjMIDVFh0Y7JDmY9/yMOFxpviWoRAdZ0ZtQaz0/0p5VLcMjQKd6b0xbRBkZj1xj6U64worK4HANwzPh7PzRkOmZTXNhHRtcGQRERE1IOF+6tw85CodsuYTCYYsgVuHhwJhULh0n6nDopoMyRdqaxzeK5SyJA21LEN6w/m4lKFYzlnxvcLxRu/vg4Bajkq9Uas35+D0wWOC098/MNFTB8ahdREx2XEVXIp5DJOmiEi9zEkERERkdsUMimeSBuAt9u4kW3LsvYKq+pxOLfcpeP8kFOO61dkdFhu4bpDrbYF+Snw9j1jMCk53KVjERE1YUgiIiKiTnkibSCeSBvodr3TBVWoN1k7LniVqupM2JNZzJBERG5jSCIiIqIudcOACDw4pR/OFDTfWNZstaK81oiSWgMq9SbEBKuRFO7vUC+zsAZKuRTh/krUGszIKtG1e5ykCC3un5zkkXMgop6NIYmIiIi6lFwmxTOzh6LWYEbGmSJ88VMBvj1XDqO5eXRJCOCj342D1G4xhoKqOnx5shDpJwvaDEjXxQdjzqhYzB4ZiwiuhEdEncSQRERERF1GbzQj40wxtv9UgN2ZxTCYW0+7iw5U49bhMagzWVCuM2LHz4X48lRhm/dbGhDpjzmjY3HHqD6ID9N4+hSIqBdgSCIiIqIu80NOOR795Fir7REBKsweEYNZI6Lhp5Rh1+li/GrNAZwtrHGyl4ZgNGtEDGaPjMHAqABPN5uIehmGJCIiIrqmDGYLjuRW4NvzJRgeG4TbR8XaXpvUPxxBfgpU1ZkQ7q/ErcMbgk6YVokHPjqM9Qdy2913ZIAKs0bEYECUP2rrzfj00CXMva4PhvcJ8vBZEVFvwpBEREREV+1imR7fnivGt+dKcCCrDHqjBQAwbVCEQ0hSyqVYdvtQRAepMb5fGGRSCepNFqS+sKvNm8naK64xtApSH+zPwddPTkVypL/zSkREbmJIIiIiIrfpjWZ8n12GbzNLsPd8KXJKnS+k8ENOw4IMSnnzvZJ+OaavQxmzVUAuk7Ss6jKpRAJp56sTEbXCkERERERu++zoFfx16ymnr4X7K3HDwAhMHRiBKQMiHAKSM/4qObY/NgX/OX4FKrkMVqvA3vMl2He+tMN2hGqV+Oh345AUwVEkIrp2GJKIiIjIqao6Ew5cKMW350pwV2pfpCSE2l6bOjDC9lgulWBMQgimNgajoTGBDkt3d8RqFSitMcBkFth5Oh8nLlXCKpyXHdU3CGlDopA2NAqDowMgkXAIiYiuPYYkIiIiAtAQVn66XNk4ha4ERy9WwtKYVoL8FA4hKS5Ug8XTkjGibxAm9g9DgFrh1rGuVNZh//lS7LtQigMXSlGmMzotp1HKMLF/OG4aHIm0IZGIDFR3/gSJiFzEkERERNSLXSrXY9+5YvzveSmWn9iDCr3JabkDWWWttj01Y5DLx6mqM+H77DJ8d74U+y+UIruNa5iAhuW9bxwUgRsHRSI1MQQquczl4xARXQsMSURERL1EvckClVzqMEXt3z/k4V/fZgOQAnAMSP0jtJg6MBJTB0VgfL9QuENvNONwbgW+zy7DweyydqfQ+avkuD4pDNMGN0zX6xvCG8ISkXcxJBEREfVQJTUGHMmrwJG8chzOq8CpK1XYtWQqEsK0tjKpCaH4F7IBAFqVDJOTw3HDwAjcMCACcaGuhxW90YwjeQ2h6Pvscpy4VAlzG6lILpXguvhgTEoOx5QB4RjZNxgKWfuLOxARdSWGJCIioh7AahU4X1yLI3kVOJxXjiN5Fcgr07cqdzi3okVICsFTtwyAMf8MHr4rDRq1yqXjVdWZcPRiBQ7llOOHnPZDEdAwhW7ygHBMTg7H+KQw+Kv4EYSIfBffoYiIiLo5IQSmvLobVyrr2i2XFKFttS1Eq8Tvb+iH9PQz7Y7mXKmsw+HcchzOrcCh3HJkFtVAtJ2J0D9Ci+uTwnB9UhjGJ4UiMoALLhBR98GQRERE1A0UVtXbRoiEAJbfMcz2mkQiQVKE1iEkKeVSjOwThJTEEKQmhCIlIQShWqVLxzJbrMgsqsHRvAocyq3A4dxy5FfVt1snqTEUTWAoIqIegCGJiIjIx1isAmcLqxuvJ6rA4dwKhwDkr5Lj2duGQmZ3L6KbBkfCTyFDamIIUhJCMbxPoMurwhVV1+NEmQQnd5zDT1eqcfJyFepMljbLSyXA0NhApCaEIjUxBOMSQ7k0NxH1KAxJREREPuJMQTVeSj+DYxcrUWswt1mu1mBGTqkOyZH+tm33TeqH+yb16/AYBrMFu04X40heBY5dqsCxi5WNr8iAc7lt1kuK0GJsQihSEkMwJj4YWrtriixCoKCq/al+9swWgVCt0mEfRES+hO9OREREXcRotuJCcS3OFFTjTEE1pg2OxKTkcNvrfgoZ9p0vbVVPrZBidFwwUhIaps6NiQ9BkMa9m7cCQHW9CXNW7UdOO/coakt2iQ7ZJTpsOnzJ7bpt+deCFMwYFn3N9kdEdK0wJBEREXlAaa3BFobOFtTgdEE1skpqYbI0r3YglUocQlJCmAbh/krIpJKGMJQQgtSEEAyNDbwmS2RfLq/rVEDylB9zyhmSiMgnMSQRERFdBatVQGp3bRAALNpwFNt/Kuiw7un8aofnEokEXz85FSEahcMNX6+VobGB+Psdw7Bs288AGqbQDYj0hwRAYUEBomNiWp2Lq84V1eJCca1bddbtz8EXP+V36niuKNcZHUIpALx650jMS43z2DGJqGdgSCIiInJRVZ3JNjrU8KcGBVX1OPTMzQ6hpk+wX6u6MqkE/SO0GBITaPcnoFU5V1eg66yFExOxcGKiwzaTyYT09CuYNWsUFAr3p/EBwD92nHU7JFkFUFRt6NTxOmvtt1kMSUTUIYYkIiIiJ2rqTdh3vtQhELV1H6LLFXWIC9XYno+JD8b1SaG2MDQ0JhDJkf5QK1xbba47+sV1ffF9dsMS5fZigtS41mNiNfVm1LSzsEV7Hrkx+Rq3hoh6IoYkIiLq1XQGM84W1iBMq0RiePPNVstqjXjk46Pt1pVIgH5hWpTrjA4haebwGMwcHuOxNvui5Eh//O8fJl7TfQohUFRtwM/5VTidX42f86txuqDapYDkp5BhRJ8gjOwbhFFxwRjVNxhxoX4emcZIRD0PQxIREfUKJosVlyvqHFaXO1NQjbxyPYQAHrmxP/40c7CtfHyoBlqlDDpjw/2C/FVyDI4OcJgqNyg6ABol/yu9lrJKavG79YeQV6bvVP2kcC1G9g1CcqQ/ZNKGxS4uVzQsWJFdUouzhTV4eGp/3Doi2uX7SBFR78N3diIi6jEsVuFwg1UAWLnrHD4/dgWXK+pgsYo2agKnCxwXUZBKJfjrbUMRolFiaEwg+ob4dXpRA3KN2WLFL1bvR3V956bSAUB2qQ7ZHazg98Sm4yitHYIHpiR1+jhE1LMxJBERUbfSNCKUW6pDbpkOuaU65JTpkVemQ1mtET8tm+4QZnQGc5ujEmqFFIOiAzE0JgBjE0NbvX73uHiPnQe1JgAEa5RXFZJcdbawBgezytyuF6xRYHB0AKftEfVwDElEROTzLhTX4rkvTiOvTNfhiFBRTT1igppXl0sI00KrlCExXIvEMC0SwzUYHN0wZa5fuLbVyBN5j0ImxfbHJuPzY1cAAJEBarSXRX6+UoU3v7nQqWNtOXIZW45c7lTdx25KxpLpgzpVl4i6B4YkIiLyivZGhB67aQB+ldLXVlYpk2LvuZJ299cUhGrqzYgJat7+67FxuGd8PL/57yYC1Ar8dkKiS2XlXgq4lyqcr3JIRD0HQxIREXWZtzLO41BeRYcjQlkljvfbiQ1WQy6VQCWXOowIJYRp0a/xebi/0mkQksukHjkX8r5pgyKxcv5ovPzlWUQEqDA4OgDhASoAgFUIFFTWI7u0FjklOtsCHJ2hlEuRGKZBUrg/BscE4N4W95kiop6HIYmIiK5KWyNCGoUMaxekOJQ9lFfh0oiQuUV4ksuk+PGZNIRoFBwRIhupVIJpgyIRH6ZBTomuIRCV6pBdokNOqQ4Gs9Wt/cUGqZEU4Y9+4VokRTQE8P4R/ogN9uO0TKJehiGJiIjc8t35Umz4MQ85pQ1T4/RtfEMvl0oghLCFGqtVQKtsXnI5MkDV5ohQsEbpsK96kwVmqxUltYZ22yaBBBGNIwlNqutNqDd1PIqgkssQ5Kdw2FZaa4BVtH39U5MAlQJ+dudmslhRoTd2WA8AwrQqhw/geqMZtS7cB0gulSJU6/hzqtQbYbR0HAz8FDIEqJvPVQiBaiNQUmOAXNH+zyrIT+GwdLbBbEFVnanDYwIN1xjZq6k3oc6F340QAjX1ZmSVNAWgWmQW1uBimR4VLh67ib9Shviwhj6XEKpBfJgG8aFaxIX6OfwOASBUo3QYiawzWlBj6Ph4UokE4f6O/bBKb4LB0vG5qhUyBKod+2FJjQECHffDQLXC4YbFRrMVlXWu9cNwrcphwZNagxl6o/N+aDaZbf3FTyUQ0qIfVuiMMFk77odapRxaVfNHUatVoFTX/r/xJsF+Sijlzb+bepMF1fUd/274HtH594iO3n+btH6PsOIqBnK9hiGJiIjc8l/v/+BSObNVoMZgtn3gqzGY8eWpQtvrxTUGFNcY8GNueau6nz8yEdfFh9ie7zxdhEc/OdbhMQPUcpxcPsNh2/P/dxqbXbhAf/bIGKz+zRjHbW/uQ1F1xx8MXv3VSMwbG2d7nlVSi5kr93VYDwC+X3ozooOaw8O/v8/DS+lnO6w3KCoAO568wWHb4g3H8N2F0g7rPjilH56ZPdT2XAjg2SNyPHvk2w7r/s/94zBlQITt+YGsMty37lCH9SQSIGfFbIdtr+88h3X7czusezUmJIVhzuhY2wiRwWzB5Fd243RBTYd1dy25AcmRAbbnW49fwdLPTnZYLzZIjQNLb3bY9uf//Qlf/VzYRo1mvx4bh5d/NdJh29R/7G7zywh7a+4Zg1tHNN/E+KfLlbhz7cEO6wHAz3+f4RBY3vk2q4NFMRr6S0pCSKubCN+7/hBOXKrs8JhPpg3E42kDbM9rDGaMezHDpfbyPaLr3yNc/d20fI/4PrsMzxyWIWhgCaYPj3VpH76AIYmIiNxif4PV9jyRNqDVN+JE9goq6z1+jHqzBScuV+LE5UoADUvCu+r1neccRg4uFNe2U7pZZZ0JSz/7yWHbqfwql+oeyi3H0s9+ghANoxS5ZXqXAhKRL6rQGXG2sBZGqwT/OVHAkERERD3X/LHx2Hr8ChLDNAjzV6GtKzX6R/g7PFfIJJg+NMqlY7Sc0hIdpHapbsvpUgAwLDYQVXUd1x3VN6jVtqkDI1Cp73gKT58QP4fn/iq5y+eqkjsuLJEQpnWpbmywX6ttKQkh0Dj5GbQ0MCqg1bYRIVZER0d3eM1Xy+k7Ef4ql9rbcrcGswUZZ4s6rHe1jl2sxLGLlZ2qm36y45EfZ/RGCz758VKn6maV6JBV0vbNcBPDNE5/fwAQGeg4nTFYo3C5H7a85qp/pH+bdYUQKCwsRHR0NJKdtOX6fqGIajGlzZmkCK3Dc75HOOcr7xGuttf+PaLebMFrO88DQLu3bvBFEiFcmEjZg1RXVyMoKAhVVVUIDAz0altMJhPS09Mxa9YsKBT8tpXax/5C7mB/IXd4o78IIfCrNQdwtJMBprd66+7rcPso734bz/cXclVBVR0mrPgGADBzWBTWLkj1anvcyQEcSSIiIqIuJ5FIsOXhicgqqYWlm35fazBZUVBVj/zKOhRU1SG/sh5XKuuQX1mH4hrXLnJvT0yQGvGhGiSGaZEQrsH0oVEO10gRkecwJBEREZFXSKUSDGhj6pgvsFgFvjpViHNFNbYQlF9ZhyuVdW4vL96WhMaV9hLDNLZAFBOshlza+v5eFiuQWdjxghMd0RnNsFgFUuJDHFa0I6JmDElERERELQghMP9fB3E4r8Kjx8kr0yOvTI+9Hj2Kc6kJIdj88ATee4zICd6GnIiIiKgFg9mK0wXV3m6GR50uqL5mI2JEPQ1HkoiIiIhaUCtkeO+3qVj8yTGU64zwU8gQE6xGn2A/RAaooZB17ejLxkOdWy2vPXqjBdc9t7MTNQUsFhmePrwLaHN9S++KDlLjtbtGISUhpOPCRE4wJBERERE5MTE5HEefvcXbzUC9yYItRy7D7IEllOtMnb0HkwSw+u4oVE6pDv93Ip8hiTqNIYmIiIjIh6kVMiydNQTPf3Ha6esBKjkiA1WQSSWoM1mgN1igN1quIgB1P1IJoFHK4aeUQauUIS5Ug/+6Pt7bzaJujCGJiIiIqIsIIWC0WBuCjMmCOqMZusZQozeaG8KN0QJdi8d1RgvuGBVrK9Oy/JXKOtSbfHdkp4lKLoVWJYefQgaNUgaNSg5Ni8d+Shm0KllD6FE0PPZTNpZr3K5RNtZpfKySS7kAhQ+KDlQj8++3IP3LLzHr1pHebo5bGJKIiIiIWrBYBfSN4UTXGEiaHtc1hhP7x/ahpWUAqjM1vta43eKBaXPXkkwqaRVCNMqGoKJVyqCWS1BccAVDBiTBX61oVa75sd22xmAk45LjvYpEIoFUKoFUgm633DxDEhEREXVLQggYzFboDGbb9DKdoSHM6O1GYBwCjJMRm+Yyza91h1XfmkdjZNAo5LYRGD9FQzixf9xQpjG4qBpDj0LeOGLTHID8lDIoZe2PyphMJqSnX8KsGQOhUCi68IyJug5DEhEREXmUyWK1hRJnYUXfOCKja/G4ZXmdwdw4KmOBvvGxjw/KQCGTNE4Za7pepuFvTYvHzkZi2ivvp5B1u2/miboThiQiIiKCEIDeaIbJ0HS9jN3UMaO5cZTG+bQzZyM1zWUsMFp8e1RGIkHjtTD2QcX5VDPbY5WscSRH7nSUpmlkRynnLSmp96qqM+HNXZnIzpXCcCwf88YleLtJLmNIIiIi6kaMZmubF/a3PcXMlalmcuD7b7x9eu1SyqVORmBkDqua2T/2c3qdTOvHagUv+ifyBL3RjPf35wGQQpVZwpBERETUm1mtAnqT3YiKwYK6xpEZ+8dNSzXrTWa7ZZsbL/Zvsb0p3HjiXjnXUtNSzC1HYJqmjtmmkbW3wlnjdDKtqrm8RiGDXMZRGSLqGj4RklavXo1//OMfKCwsxKhRo/DWW29h3LhxbZbfvHkznn32WeTm5mLAgAF45ZVXMGvWrC5sMRERdXdNF/03hJGGa1xaTx1rawSm/RXOusNSzGqF1LbEskYphVFfi5iIUPirFS2WW3Y+7cyvcbv9Yy7FTEQ9hddD0qZNm7BkyRKsXbsW48ePx8qVKzFjxgxkZmYiMjKyVfkDBw7g7rvvxooVK3Dbbbdhw4YNmDt3Lo4ePYrhw4d74QyIyNuEEBACsAoBgca/BdrYJmBt8bf96y3rNtVvPoaTsmhjn1bn7Wlvn7Z92O1TtDwm7I/V9JrjMU1mC07nS1CwPxcSidS2T/tjtH3Mpm3N7Wuqa9+OhgENAau1ua614Qdie73p/GF/7k37bOtn32KfosUxBZxsc9IHHNvRsE+gYRGBpmDk60sxy6WS1iMwrlzs394KZ8rWSzE3rFaWjlmzxnK1MiIi+EBIev311/Hggw/ivvvuAwCsXbsW27dvxwcffICnn366Vfk33ngDM2fOxB//+EcAwPPPP4+dO3di1apVWLt2bZe2nVpz+cNqiw9A3fbDaosPls4+rDr/kOf+h1WLxYqsXCmObD8LqVTa7T+stj5me33A7ufk5JjUFhmQd87bjegVmm942XDBvtMllhsfN00da5pW1uZ9ZZRyXvRPROQlXg1JRqMRR44cwdKlS23bpFIp0tLScPDgQad1Dh48iCVLljhsmzFjBrZu3erJpnpExtliPH5QjscPfu3tplC3IQUKLnq7EUS9TtMyzi1XL9OoZFDLZZC2k2VMFiuq6qyoqjN1XYPdZLUKFBZI8WX1CS4rTR1ifyFX1Zkstsc5pTovtsR9Xg1JpaWlsFgsiIqKctgeFRWFs2fPOq1TWFjotHxhYaHT8gaDAQaDwfa8uroaQMPUApPJe/9hGUwWPLHpJ68dn4iIXGeyCJgsZlTXm73dFA+SAuVF3m4EdRvsL+Qei9Xq1c/eANw6vten23naihUr8Pe//73V9q+//hoajcYLLWqW5C/F6UpOpSAiIiKins3fUov09HSvtkGv17tc1qshKTw8HDKZDEVFjt9EFBUVITo62mmd6Ohot8ovXbrUYXpedXU14uLiMH36dAQGBl7lGVydW24x4cP/7MR1qeOhVMghbVwNSCqRQCJpWEZVIpFA4rCt4e+mx1KuINRrmM0m7Nu7D1NumAK5nBdWU/vYX8gd7C/kDvYXckdTf7nlphsREeTdAYqmGWWu8GpIUiqVSElJQUZGBubOnQsAsFqtyMjIwOLFi53WmTBhAjIyMvDEE0/Ytu3cuRMTJkxwWl6lUkGlUrXarlAofGIFn1gNkJIY5hNtId9mMpkQrALiwgLYX6hD7C/kDvYXcgf7C7mjqb9EBGm83l/cOb7Xp9stWbIECxcuRGpqKsaNG4eVK1dCp9PZVrv77W9/iz59+mDFihUAgMcffxxTp07FP//5T8yePRsbN27E4cOH8c4773jzNIiIiIiIqIfwekiaP38+SkpK8Le//Q2FhYUYPXo0vvrqK9viDBcvXoTUbtmgiRMnYsOGDfjrX/+Kv/zlLxgwYAC2bt3KeyQREREREdE14fWQBACLFy9uc3rdnj17Wm276667cNddd3m4VURERERE1BtxaTUiIiIiIiI7DElERERERER2GJKIiIiIiIjsMCQRERERERHZYUgiIiIiIiKyw5BERERERERkhyGJiIiIiIjIDkMSERERERGRHYYkIiIiIiIiOwxJREREREREdhiSiIiIiIiI7DAkERERERER2WFIIiIiIiIissOQREREREREZIchiYiIiIiIyA5DEhERERERkR2GJCIiIiIiIjsMSURERERERHYYkoiIiIiIiOwwJBEREREREdlhSCIiIiIiIrLDkERERERERGSHIYmIiIiIiMgOQxIREREREZEdhiQiIiIiIiI7DElERERERER2GJKIiIiIiIjsMCQRERERERHZkXu7AV1NCAEAqK6u9nJLAJPJBL1ej+rqaigUCm83h3wc+wu5g/2F3MH+Qu5gfyF3+FJ/afr835QH2tPrQlJNTQ0AIC4uzsstISIiIiKirlZTU4OgoKB2y0iEK1GqB7FarcjPz0dAQAAkEolX21JdXY24uDhcunQJgYGBXm0L+T72F3IH+wu5g/2F3MH+Qu7wpf4ihEBNTQ1iY2MhlbZ/1VGvG0mSSqXo27evt5vhIDAw0OudhroP9hdyB/sLuYP9hdzB/kLu8JX+0tEIUhMu3EBERERERGSHIYmIiIiIiMgOQ5IXqVQqLFu2DCqVyttNoW6A/YXcwf5C7mB/IXewv5A7umt/6XULNxAREREREbWHI0lERERERER2GJKIiIiIiIjsMCQRERERERHZYUgiIiIiIiKyw5DkYatXr0ZiYiLUajXGjx+PH3/8sd3ymzdvxuDBg6FWqzFixAikp6d3UUvJF7jTX959911MmTIFISEhCAkJQVpaWof9i3oWd99fmmzcuBESiQRz5871bAPJp7jbXyorK7Fo0SLExMRApVJh4MCB/D+pF3G3v6xcuRKDBg2Cn58f4uLi8OSTT6K+vr6LWkvetHfvXtx+++2IjY2FRCLB1q1bO6yzZ88ejBkzBiqVCsnJyVi/fr3H2+kuhiQP2rRpE5YsWYJly5bh6NGjGDVqFGbMmIHi4mKn5Q8cOIC7774b999/P44dO4a5c+di7ty5OHXqVBe3nLzB3f6yZ88e3H333di9ezcOHjyIuLg4TJ8+HVeuXOnilpM3uNtfmuTm5uKpp57ClClTuqil5Avc7S9GoxG33HILcnNzsWXLFmRmZuLdd99Fnz59urjl5A3u9pcNGzbg6aefxrJly3DmzBm8//772LRpE/7yl790ccvJG3Q6HUaNGoXVq1e7VD4nJwezZ8/GtGnTcPz4cTzxxBN44IEHsGPHDg+31E2CPGbcuHFi0aJFtucWi0XExsaKFStWOC0/b948MXv2bIdt48ePF7///e892k7yDe72l5bMZrMICAgQH374oaeaSD6kM/3FbDaLiRMnivfee08sXLhQzJkzpwtaSr7A3f6yZs0akZSUJIxGY1c1kXyIu/1l0aJF4qabbnLYtmTJEjFp0iSPtpN8DwDx+eeft1vmT3/6kxg2bJjDtvnz54sZM2Z4sGXu40iShxiNRhw5cgRpaWm2bVKpFGlpaTh48KDTOgcPHnQoDwAzZsxoszz1HJ3pLy3p9XqYTCaEhoZ6qpnkIzrbX5577jlERkbi/vvv74pmko/oTH/Ztm0bJkyYgEWLFiEqKgrDhw/HSy+9BIvF0lXNJi/pTH+ZOHEijhw5YpuSl52djfT0dMyaNatL2kzdS3f5vCv3dgN6qtLSUlgsFkRFRTlsj4qKwtmzZ53WKSwsdFq+sLDQY+0k39CZ/tLSn//8Z8TGxrZ646GepzP95bvvvsP777+P48ePd0ELyZd0pr9kZ2fjm2++wT333IP09HRcuHABjzzyCEwmE5YtW9YVzSYv6Ux/+c1vfoPS0lJMnjwZQgiYzWY8/PDDnG5HTrX1ebe6uhp1dXXw8/PzUssccSSJqAd4+eWXsXHjRnz++edQq9Xebg75mJqaGixYsADvvvsuwsPDvd0c6gasVisiIyPxzjvvICUlBfPnz8czzzyDtWvXertp5IP27NmDl156CW+//TaOHj2Kzz77DNu3b8fzzz/v7aYRdRpHkjwkPDwcMpkMRUVFDtuLiooQHR3ttE50dLRb5ann6Ex/afLaa6/h5Zdfxq5duzBy5EhPNpN8hLv9JSsrC7m5ubj99ttt26xWKwBALpcjMzMT/fv392yjyWs68/4SExMDhUIBmUxm2zZkyBAUFhbCaDRCqVR6tM3kPZ3pL88++ywWLFiABx54AAAwYsQI6HQ6PPTQQ3jmmWcglfI7eWrW1ufdwMBAnxlFAjiS5DFKpRIpKSnIyMiwbbNarcjIyMCECROc1pkwYYJDeQDYuXNnm+Wp5+hMfwGAV199Fc8//zy++uorpKamdkVTyQe4218GDx6MkydP4vjx47Y/d9xxh21lobi4uK5sPnWxzry/TJo0CRcuXLCFaQA4d+4cYmJiGJB6uM70F71e3yoINQVsIYTnGkvdUrf5vOvtlSN6so0bNwqVSiXWr18vTp8+LR566CERHBwsCgsLhRBCLFiwQDz99NO28vv37xdyuVy89tpr4syZM2LZsmVCoVCIkydPeusUqAu5219efvlloVQqxZYtW0RBQYHtT01NjbdOgbqQu/2lJa5u17u4218uXrwoAgICxOLFi0VmZqb44osvRGRkpHjhhRe8dQrUhdztL8uWLRMBAQHik08+EdnZ2eLrr78W/fv3F/PmzfPWKVAXqqmpEceOHRPHjh0TAMTrr78ujh07JvLy8oQQQjz99NNiwYIFtvLZ2dlCo9GIP/7xj+LMmTNi9erVQiaTia+++spbp+AUQ5KHvfXWWyI+Pl4olUoxbtw48f3339temzp1qli4cKFD+U8//VQMHDhQKJVKMWzYMLF9+/YubjF5kzv9JSEhQQBo9WfZsmVd33DyCnffX+wxJPU+7vaXAwcOiPHjxwuVSiWSkpLEiy++KMxmcxe3mrzFnf5iMpnE8uXLRf/+/YVarRZxcXHikUceERUVFV3fcOpyu3fvdvp5pKmPLFy4UEydOrVVndGjRwulUimSkpLEunXrurzdHZEIwXFQIiIiIiKiJrwmiYiIiIiIyA5DEhERERERkR2GJCIiIiIiIjsMSURERERERHYYkoiIiIiIiOwwJBEREREREdlhSCIiIiIiIrLDkERERL2KRCLB1q1br3lZIiLqORiSiIjIa+69915IJBJIJBIolUokJyfjueeeg9ls9tgxCwoKcOutt17zskRE1HPIvd0AIiLq3WbOnIl169bBYDAgPT0dixYtgkKhwNKlSx3KGY1GKJXKqz5edHS0R8oSEVHPwZEkIiLyKpVKhejoaCQkJOAPf/gD0tLSsG3bNtx7772YO3cuXnzxRcTGxmLQoEEAgEuXLmHevHkIDg5GaGgo5syZg9zcXId9fvDBBxg2bBhUKhViYmKwePFi22v2U+iMRiMWL16MmJgYqNVqJCQkYMWKFU7LAsDJkydx0003wc/PD2FhYXjooYdQW1tre72pza+99hpiYmIQFhaGRYsWwWQyXfsfHBEReQxDEhER+RQ/Pz8YjUYAQEZGBjIzM7Fz50588cUXMJlMmDFjBgICArBv3z7s378f/v7+mDlzpq3OmjVrsGjRIjz00EM4efIktm3bhuTkZKfHevPNN7Ft2zZ8+umnyMzMxMcff4zExESnZXU6HWbMmIGQkBAcOnQImzdvxq5duxwCGADs3r0bWVlZ2L17Nz788EOsX78e69evv2Y/HyIi8jxOtyMiIp8ghEBGRgZ27NiBRx99FCUlJdBqtXjvvfds0+z+/e9/w2q14r333oNEIgEArFu3DsHBwdizZw+mT5+OF154Af/93/+Nxx9/3LbvsWPHOj3mxYsXMWDAAEyePBkSiQQJCQlttm/Dhg2or6/HRx99BK1WCwBYtWoVbr/9drzyyiuIiooCAISEhGDVqlWQyWQYPHgwZs+ejYyMDDz44IPX5OdERESex5EkIiLyqi+++AL+/v5Qq9W49dZbMX/+fCxfvhwAMGLECIfrkE6cOIELFy4gICAA/v7+8Pf3R2hoKOrr65GVlYXi4mLk5+fj5ptvdunY9957L44fP45Bgwbhsccew9dff91m2TNnzmDUqFG2gAQAkyZNgtVqRWZmpm3bsGHDIJPJbM9jYmJQXFzs6o+DiIh8AEeSiIjIq6ZNm4Y1a9ZAqVQiNjYWcnnzf032gQQAamtrkZKSgo8//rjVfiIiIiCVuvfd35gxY5CTk4Mvv/wSu3btwrx585CWloYtW7Z07mQAKBQKh+cSiQRWq7XT+yMioq7HkERERF6l1WrbvGaopTFjxmDTpk2IjIxEYGCg0zKJiYnIyMjAtGnTXNpnYGAg5s+fj/nz5+POO+/EzJkzUV5ejtDQUIdyQ4YMwfr166HT6Wzhbf/+/ZBKpbZFJYiIqGfgdDsiIuo27rnnHoSHh2POnDnYt28fcnJysGfPHjz22GO4fPkyAGD58uX45z//iTfffBPnz5/H0aNH8dZbbznd3+uvv45PPvkEZ8+exblz57B582ZER0cjODjY6bHVajUWLlyIU6dOYffu3Xj00UexYMEC2/VIRETUMzAkERFRt6HRaLB3717Ex8fjl7/8JYYMGYL7778f9fX1tpGlhQsXYuXKlXj77bcxbNgw3HbbbTh//rzT/QUEBODVV19Famoqxo4di9zcXKSnpzudtqfRaLBjxw6Ul5dj7NixuPPOO3HzzTdj1apVHj1nIiLqehIhhPB2I4iIiIiIiHwFR5KIiIiIiIjsMCQRERERERHZYUgiIiIiIiKyw5BERERERERkhyGJiIiIiIjIDkMSERERERGRHYYkIiIiIiIiOwxJREREREREdhiSiIiIiIiI7DAkERERERER2WFIIiIiIiIissOQREREREREZOf/A4XWhqPUdRmXAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1200x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_prc(name, labels, predictions, **kwargs):\n",
    "    precision, recall, _ = sklearn.metrics.precision_recall_curve(labels, predictions)\n",
    "\n",
    "    plt.plot(precision, recall, label=name, linewidth=2, **kwargs)\n",
    "    plt.xlabel('Precision')\n",
    "    plt.ylabel('Recall')\n",
    "    plt.grid(True)\n",
    "    ax = plt.gca()\n",
    "    ax.set_aspect('equal')\n",
    "\n",
    "plot_prc(\"Train Baseline\", train_labels, train_predictions_baseline, color=colors[0])\n",
    "plot_prc(\"Test Baseline\", test_labels, test_predictions_baseline, color=colors[0], linestyle='--')\n",
    "plt.legend();"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "假负例（漏掉欺诈交易）可能造成财务损失，而假正例（将交易错误地标记为欺诈）则可能降低用户满意度。"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 类权重\n",
    "我们的目标是识别欺诈交易，但没有很多可以使用的此类正样本，因此希望分类器提高可用的少数样本的权重。为此，可以使用参数将 Keras 权重传递给每个类。这些权重将使模型“更加关注”来自代表不足的类的样本。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Weight for class 0: 0.50\n",
      "Weight for class 1: 289.44\n"
     ]
    }
   ],
   "source": [
    "# 按total/2进行缩放有助于将损失保持在相似的量级\n",
    "# 所有例子的权重之和保持不变\n",
    "weight_for_0 = (1 / neg)*(total)/2.0 \n",
    "weight_for_1 = (1 / pos)*(total)/2.0\n",
    "\n",
    "class_weight = {0: weight_for_0, 1: weight_for_1}\n",
    "\n",
    "print('Weight for class 0: {:.2f}'.format(weight_for_0))\n",
    "print('Weight for class 1: {:.2f}'.format(weight_for_1))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 使用类权重训练模型\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From d:\\anaconda3\\envs\\tensorflow\\lib\\site-packages\\tensorflow\\python\\ops\\array_ops.py:5043: calling gather (from tensorflow.python.ops.array_ops) with validate_indices is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "The `validate_indices` argument has no effect. Indices are always validated on CPU and never validated on GPU.\n",
      "Epoch 1/100\n",
      "90/90 [==============================] - 3s 13ms/step - loss: 0.7281 - tp: 380.0000 - fp: 147173.0000 - tn: 91651.0000 - fn: 34.0000 - accuracy: 0.3847 - precision: 0.0026 - recall: 0.9179 - auc: 0.8390 - prc: 0.0371 - val_loss: 0.8372 - val_tp: 75.0000 - val_fp: 24519.0000 - val_tn: 20972.0000 - val_fn: 3.0000 - val_accuracy: 0.4619 - val_precision: 0.0030 - val_recall: 0.9615 - val_auc: 0.9530 - val_prc: 0.4755\n",
      "Epoch 2/100\n",
      "90/90 [==============================] - 1s 6ms/step - loss: 0.4368 - tp: 293.0000 - fp: 72103.0000 - tn: 109857.0000 - fn: 23.0000 - accuracy: 0.6043 - precision: 0.0040 - recall: 0.9272 - auc: 0.9238 - prc: 0.2344 - val_loss: 0.4927 - val_tp: 71.0000 - val_fp: 7618.0000 - val_tn: 37873.0000 - val_fn: 7.0000 - val_accuracy: 0.8327 - val_precision: 0.0092 - val_recall: 0.9103 - val_auc: 0.9585 - val_prc: 0.6459\n",
      "Epoch 3/100\n",
      "90/90 [==============================] - 1s 6ms/step - loss: 0.3277 - tp: 293.0000 - fp: 39552.0000 - tn: 142408.0000 - fn: 23.0000 - accuracy: 0.7829 - precision: 0.0074 - recall: 0.9272 - auc: 0.9492 - prc: 0.3652 - val_loss: 0.3374 - val_tp: 70.0000 - val_fp: 3276.0000 - val_tn: 42215.0000 - val_fn: 8.0000 - val_accuracy: 0.9279 - val_precision: 0.0209 - val_recall: 0.8974 - val_auc: 0.9596 - val_prc: 0.6907\n",
      "Epoch 4/100\n",
      "90/90 [==============================] - 1s 6ms/step - loss: 0.2759 - tp: 287.0000 - fp: 24422.0000 - tn: 157538.0000 - fn: 29.0000 - accuracy: 0.8659 - precision: 0.0116 - recall: 0.9082 - auc: 0.9570 - prc: 0.4336 - val_loss: 0.2538 - val_tp: 70.0000 - val_fp: 1986.0000 - val_tn: 43505.0000 - val_fn: 8.0000 - val_accuracy: 0.9562 - val_precision: 0.0340 - val_recall: 0.8974 - val_auc: 0.9606 - val_prc: 0.7122\n",
      "Epoch 5/100\n",
      "90/90 [==============================] - 1s 7ms/step - loss: 0.2639 - tp: 285.0000 - fp: 17680.0000 - tn: 164280.0000 - fn: 31.0000 - accuracy: 0.9028 - precision: 0.0159 - recall: 0.9019 - auc: 0.9545 - prc: 0.5172 - val_loss: 0.2187 - val_tp: 70.0000 - val_fp: 1667.0000 - val_tn: 43824.0000 - val_fn: 8.0000 - val_accuracy: 0.9632 - val_precision: 0.0403 - val_recall: 0.8974 - val_auc: 0.9611 - val_prc: 0.7175\n",
      "Epoch 6/100\n",
      "90/90 [==============================] - 1s 6ms/step - loss: 0.2461 - tp: 285.0000 - fp: 14700.0000 - tn: 167260.0000 - fn: 31.0000 - accuracy: 0.9192 - precision: 0.0190 - recall: 0.9019 - auc: 0.9590 - prc: 0.5112 - val_loss: 0.2032 - val_tp: 70.0000 - val_fp: 1609.0000 - val_tn: 43882.0000 - val_fn: 8.0000 - val_accuracy: 0.9645 - val_precision: 0.0417 - val_recall: 0.8974 - val_auc: 0.9630 - val_prc: 0.7082\n",
      "Epoch 7/100\n",
      "90/90 [==============================] - 1s 6ms/step - loss: 0.2434 - tp: 279.0000 - fp: 12824.0000 - tn: 169136.0000 - fn: 37.0000 - accuracy: 0.9294 - precision: 0.0213 - recall: 0.8829 - auc: 0.9549 - prc: 0.5290 - val_loss: 0.1912 - val_tp: 70.0000 - val_fp: 1524.0000 - val_tn: 43967.0000 - val_fn: 8.0000 - val_accuracy: 0.9664 - val_precision: 0.0439 - val_recall: 0.8974 - val_auc: 0.9645 - val_prc: 0.7118\n",
      "Epoch 8/100\n",
      "90/90 [==============================] - 1s 6ms/step - loss: 0.2313 - tp: 282.0000 - fp: 10863.0000 - tn: 171097.0000 - fn: 34.0000 - accuracy: 0.9402 - precision: 0.0253 - recall: 0.8924 - auc: 0.9616 - prc: 0.5115 - val_loss: 0.1743 - val_tp: 69.0000 - val_fp: 1377.0000 - val_tn: 44114.0000 - val_fn: 9.0000 - val_accuracy: 0.9696 - val_precision: 0.0477 - val_recall: 0.8846 - val_auc: 0.9654 - val_prc: 0.7142\n",
      "Epoch 9/100\n",
      "90/90 [==============================] - 1s 6ms/step - loss: 0.1931 - tp: 289.0000 - fp: 9668.0000 - tn: 172292.0000 - fn: 27.0000 - accuracy: 0.9468 - precision: 0.0290 - recall: 0.9146 - auc: 0.9763 - prc: 0.5527 - val_loss: 0.1542 - val_tp: 69.0000 - val_fp: 1232.0000 - val_tn: 44259.0000 - val_fn: 9.0000 - val_accuracy: 0.9728 - val_precision: 0.0530 - val_recall: 0.8846 - val_auc: 0.9654 - val_prc: 0.7158\n",
      "Epoch 10/100\n",
      "90/90 [==============================] - 1s 6ms/step - loss: 0.2142 - tp: 288.0000 - fp: 8995.0000 - tn: 172965.0000 - fn: 28.0000 - accuracy: 0.9505 - precision: 0.0310 - recall: 0.9114 - auc: 0.9642 - prc: 0.5627 - val_loss: 0.1516 - val_tp: 69.0000 - val_fp: 1232.0000 - val_tn: 44259.0000 - val_fn: 9.0000 - val_accuracy: 0.9728 - val_precision: 0.0530 - val_recall: 0.8846 - val_auc: 0.9655 - val_prc: 0.7061\n",
      "Epoch 11/100\n",
      "90/90 [==============================] - 1s 7ms/step - loss: 0.1840 - tp: 286.0000 - fp: 7980.0000 - tn: 173980.0000 - fn: 30.0000 - accuracy: 0.9561 - precision: 0.0346 - recall: 0.9051 - auc: 0.9774 - prc: 0.5686 - val_loss: 0.1360 - val_tp: 69.0000 - val_fp: 1103.0000 - val_tn: 44388.0000 - val_fn: 9.0000 - val_accuracy: 0.9756 - val_precision: 0.0589 - val_recall: 0.8846 - val_auc: 0.9657 - val_prc: 0.7079\n",
      "Epoch 12/100\n",
      "90/90 [==============================] - 1s 6ms/step - loss: 0.1818 - tp: 289.0000 - fp: 7774.0000 - tn: 174186.0000 - fn: 27.0000 - accuracy: 0.9572 - precision: 0.0358 - recall: 0.9146 - auc: 0.9764 - prc: 0.5722 - val_loss: 0.1298 - val_tp: 69.0000 - val_fp: 1090.0000 - val_tn: 44401.0000 - val_fn: 9.0000 - val_accuracy: 0.9759 - val_precision: 0.0595 - val_recall: 0.8846 - val_auc: 0.9651 - val_prc: 0.7092\n",
      "Epoch 13/100\n",
      "90/90 [==============================] - 1s 6ms/step - loss: 0.1638 - tp: 294.0000 - fp: 6939.0000 - tn: 175021.0000 - fn: 22.0000 - accuracy: 0.9618 - precision: 0.0406 - recall: 0.9304 - auc: 0.9825 - prc: 0.6024 - val_loss: 0.1177 - val_tp: 69.0000 - val_fp: 965.0000 - val_tn: 44526.0000 - val_fn: 9.0000 - val_accuracy: 0.9786 - val_precision: 0.0667 - val_recall: 0.8846 - val_auc: 0.9654 - val_prc: 0.7093\n",
      "Epoch 14/100\n",
      "90/90 [==============================] - 1s 6ms/step - loss: 0.1857 - tp: 288.0000 - fp: 6661.0000 - tn: 175299.0000 - fn: 28.0000 - accuracy: 0.9633 - precision: 0.0414 - recall: 0.9114 - auc: 0.9721 - prc: 0.6086 - val_loss: 0.1170 - val_tp: 69.0000 - val_fp: 962.0000 - val_tn: 44529.0000 - val_fn: 9.0000 - val_accuracy: 0.9787 - val_precision: 0.0669 - val_recall: 0.8846 - val_auc: 0.9655 - val_prc: 0.7093\n",
      "Epoch 15/100\n",
      "90/90 [==============================] - 1s 6ms/step - loss: 0.1766 - tp: 287.0000 - fp: 6279.0000 - tn: 175681.0000 - fn: 29.0000 - accuracy: 0.9654 - precision: 0.0437 - recall: 0.9082 - auc: 0.9767 - prc: 0.6228 - val_loss: 0.1145 - val_tp: 69.0000 - val_fp: 968.0000 - val_tn: 44523.0000 - val_fn: 9.0000 - val_accuracy: 0.9786 - val_precision: 0.0665 - val_recall: 0.8846 - val_auc: 0.9655 - val_prc: 0.7097\n",
      "Epoch 16/100\n",
      "90/90 [==============================] - 1s 7ms/step - loss: 0.1811 - tp: 287.0000 - fp: 6581.0000 - tn: 175379.0000 - fn: 29.0000 - accuracy: 0.9637 - precision: 0.0418 - recall: 0.9082 - auc: 0.9755 - prc: 0.6066 - val_loss: 0.1161 - val_tp: 69.0000 - val_fp: 1033.0000 - val_tn: 44458.0000 - val_fn: 9.0000 - val_accuracy: 0.9771 - val_precision: 0.0626 - val_recall: 0.8846 - val_auc: 0.9662 - val_prc: 0.7118\n",
      "Epoch 17/100\n",
      "90/90 [==============================] - 1s 6ms/step - loss: 0.1791 - tp: 289.0000 - fp: 6589.0000 - tn: 175371.0000 - fn: 27.0000 - accuracy: 0.9637 - precision: 0.0420 - recall: 0.9146 - auc: 0.9765 - prc: 0.5573 - val_loss: 0.1142 - val_tp: 69.0000 - val_fp: 1035.0000 - val_tn: 44456.0000 - val_fn: 9.0000 - val_accuracy: 0.9771 - val_precision: 0.0625 - val_recall: 0.8846 - val_auc: 0.9659 - val_prc: 0.6845\n",
      "Epoch 18/100\n",
      "90/90 [==============================] - 1s 7ms/step - loss: 0.1756 - tp: 287.0000 - fp: 6185.0000 - tn: 175775.0000 - fn: 29.0000 - accuracy: 0.9659 - precision: 0.0443 - recall: 0.9082 - auc: 0.9759 - prc: 0.5901 - val_loss: 0.1147 - val_tp: 69.0000 - val_fp: 1040.0000 - val_tn: 44451.0000 - val_fn: 9.0000 - val_accuracy: 0.9770 - val_precision: 0.0622 - val_recall: 0.8846 - val_auc: 0.9664 - val_prc: 0.6942\n",
      "Epoch 19/100\n",
      "90/90 [==============================] - 1s 7ms/step - loss: 0.1700 - tp: 287.0000 - fp: 6135.0000 - tn: 175825.0000 - fn: 29.0000 - accuracy: 0.9662 - precision: 0.0447 - recall: 0.9082 - auc: 0.9781 - prc: 0.6197 - val_loss: 0.1102 - val_tp: 69.0000 - val_fp: 1006.0000 - val_tn: 44485.0000 - val_fn: 9.0000 - val_accuracy: 0.9777 - val_precision: 0.0642 - val_recall: 0.8846 - val_auc: 0.9661 - val_prc: 0.6944\n",
      "Epoch 20/100\n",
      "90/90 [==============================] - 1s 6ms/step - loss: 0.1692 - tp: 290.0000 - fp: 5934.0000 - tn: 176026.0000 - fn: 26.0000 - accuracy: 0.9673 - precision: 0.0466 - recall: 0.9177 - auc: 0.9789 - prc: 0.5983 - val_loss: 0.1128 - val_tp: 69.0000 - val_fp: 1043.0000 - val_tn: 44448.0000 - val_fn: 9.0000 - val_accuracy: 0.9769 - val_precision: 0.0621 - val_recall: 0.8846 - val_auc: 0.9659 - val_prc: 0.6697\n",
      "Epoch 21/100\n",
      "90/90 [==============================] - 1s 7ms/step - loss: 0.1770 - tp: 289.0000 - fp: 6066.0000 - tn: 175894.0000 - fn: 27.0000 - accuracy: 0.9666 - precision: 0.0455 - recall: 0.9146 - auc: 0.9762 - prc: 0.6049 - val_loss: 0.1109 - val_tp: 69.0000 - val_fp: 995.0000 - val_tn: 44496.0000 - val_fn: 9.0000 - val_accuracy: 0.9780 - val_precision: 0.0648 - val_recall: 0.8846 - val_auc: 0.9664 - val_prc: 0.6709\n",
      "Epoch 22/100\n",
      "90/90 [==============================] - 1s 6ms/step - loss: 0.1645 - tp: 290.0000 - fp: 5878.0000 - tn: 176082.0000 - fn: 26.0000 - accuracy: 0.9676 - precision: 0.0470 - recall: 0.9177 - auc: 0.9799 - prc: 0.6341 - val_loss: 0.1066 - val_tp: 69.0000 - val_fp: 967.0000 - val_tn: 44524.0000 - val_fn: 9.0000 - val_accuracy: 0.9786 - val_precision: 0.0666 - val_recall: 0.8846 - val_auc: 0.9664 - val_prc: 0.6818\n",
      "Epoch 23/100\n",
      "90/90 [==============================] - 1s 6ms/step - loss: 0.1434 - tp: 288.0000 - fp: 5330.0000 - tn: 176630.0000 - fn: 28.0000 - accuracy: 0.9706 - precision: 0.0513 - recall: 0.9114 - auc: 0.9871 - prc: 0.6103 - val_loss: 0.1015 - val_tp: 69.0000 - val_fp: 930.0000 - val_tn: 44561.0000 - val_fn: 9.0000 - val_accuracy: 0.9794 - val_precision: 0.0691 - val_recall: 0.8846 - val_auc: 0.9661 - val_prc: 0.6998\n",
      "Epoch 24/100\n",
      "90/90 [==============================] - 1s 6ms/step - loss: 0.1551 - tp: 288.0000 - fp: 5627.0000 - tn: 176333.0000 - fn: 28.0000 - accuracy: 0.9690 - precision: 0.0487 - recall: 0.9114 - auc: 0.9833 - prc: 0.6345 - val_loss: 0.1005 - val_tp: 69.0000 - val_fp: 932.0000 - val_tn: 44559.0000 - val_fn: 9.0000 - val_accuracy: 0.9793 - val_precision: 0.0689 - val_recall: 0.8846 - val_auc: 0.9668 - val_prc: 0.6998\n",
      "Epoch 25/100\n",
      "90/90 [==============================] - 1s 6ms/step - loss: 0.1587 - tp: 291.0000 - fp: 5312.0000 - tn: 176648.0000 - fn: 25.0000 - accuracy: 0.9707 - precision: 0.0519 - recall: 0.9209 - auc: 0.9811 - prc: 0.6435 - val_loss: 0.0972 - val_tp: 69.0000 - val_fp: 866.0000 - val_tn: 44625.0000 - val_fn: 9.0000 - val_accuracy: 0.9808 - val_precision: 0.0738 - val_recall: 0.8846 - val_auc: 0.9668 - val_prc: 0.7005\n",
      "Epoch 26/100\n",
      "90/90 [==============================] - 1s 6ms/step - loss: 0.1528 - tp: 292.0000 - fp: 4997.0000 - tn: 176963.0000 - fn: 24.0000 - accuracy: 0.9725 - precision: 0.0552 - recall: 0.9241 - auc: 0.9830 - prc: 0.6354 - val_loss: 0.0990 - val_tp: 69.0000 - val_fp: 926.0000 - val_tn: 44565.0000 - val_fn: 9.0000 - val_accuracy: 0.9795 - val_precision: 0.0693 - val_recall: 0.8846 - val_auc: 0.9672 - val_prc: 0.7004\n",
      "Epoch 27/100\n",
      "90/90 [==============================] - 1s 6ms/step - loss: 0.1483 - tp: 294.0000 - fp: 5500.0000 - tn: 176460.0000 - fn: 22.0000 - accuracy: 0.9697 - precision: 0.0507 - recall: 0.9304 - auc: 0.9839 - prc: 0.6409 - val_loss: 0.0967 - val_tp: 69.0000 - val_fp: 932.0000 - val_tn: 44559.0000 - val_fn: 9.0000 - val_accuracy: 0.9793 - val_precision: 0.0689 - val_recall: 0.8846 - val_auc: 0.9652 - val_prc: 0.7007\n",
      "Epoch 28/100\n",
      "90/90 [==============================] - 1s 6ms/step - loss: 0.1406 - tp: 291.0000 - fp: 5054.0000 - tn: 176906.0000 - fn: 25.0000 - accuracy: 0.9721 - precision: 0.0544 - recall: 0.9209 - auc: 0.9869 - prc: 0.6415 - val_loss: 0.0914 - val_tp: 69.0000 - val_fp: 865.0000 - val_tn: 44626.0000 - val_fn: 9.0000 - val_accuracy: 0.9808 - val_precision: 0.0739 - val_recall: 0.8846 - val_auc: 0.9653 - val_prc: 0.7016\n",
      "Epoch 29/100\n",
      "90/90 [==============================] - 1s 6ms/step - loss: 0.1546 - tp: 290.0000 - fp: 5102.0000 - tn: 176858.0000 - fn: 26.0000 - accuracy: 0.9719 - precision: 0.0538 - recall: 0.9177 - auc: 0.9826 - prc: 0.6143 - val_loss: 0.0954 - val_tp: 69.0000 - val_fp: 949.0000 - val_tn: 44542.0000 - val_fn: 9.0000 - val_accuracy: 0.9790 - val_precision: 0.0678 - val_recall: 0.8846 - val_auc: 0.9658 - val_prc: 0.7041\n",
      "Epoch 30/100\n",
      "90/90 [==============================] - 1s 6ms/step - loss: 0.1539 - tp: 291.0000 - fp: 5266.0000 - tn: 176694.0000 - fn: 25.0000 - accuracy: 0.9710 - precision: 0.0524 - recall: 0.9209 - auc: 0.9815 - prc: 0.6431 - val_loss: 0.0952 - val_tp: 69.0000 - val_fp: 962.0000 - val_tn: 44529.0000 - val_fn: 9.0000 - val_accuracy: 0.9787 - val_precision: 0.0669 - val_recall: 0.8846 - val_auc: 0.9658 - val_prc: 0.6959\n",
      "Epoch 31/100\n",
      "90/90 [==============================] - 1s 6ms/step - loss: 0.1384 - tp: 295.0000 - fp: 5322.0000 - tn: 176638.0000 - fn: 21.0000 - accuracy: 0.9707 - precision: 0.0525 - recall: 0.9335 - auc: 0.9867 - prc: 0.6392 - val_loss: 0.0955 - val_tp: 69.0000 - val_fp: 983.0000 - val_tn: 44508.0000 - val_fn: 9.0000 - val_accuracy: 0.9782 - val_precision: 0.0656 - val_recall: 0.8846 - val_auc: 0.9663 - val_prc: 0.6955\n",
      "Epoch 32/100\n",
      "90/90 [==============================] - 1s 6ms/step - loss: 0.1470 - tp: 291.0000 - fp: 5037.0000 - tn: 176923.0000 - fn: 25.0000 - accuracy: 0.9722 - precision: 0.0546 - recall: 0.9209 - auc: 0.9839 - prc: 0.6577 - val_loss: 0.0941 - val_tp: 69.0000 - val_fp: 946.0000 - val_tn: 44545.0000 - val_fn: 9.0000 - val_accuracy: 0.9790 - val_precision: 0.0680 - val_recall: 0.8846 - val_auc: 0.9673 - val_prc: 0.7047\n",
      "Epoch 33/100\n",
      "90/90 [==============================] - 1s 6ms/step - loss: 0.1387 - tp: 294.0000 - fp: 5134.0000 - tn: 176826.0000 - fn: 22.0000 - accuracy: 0.9717 - precision: 0.0542 - recall: 0.9304 - auc: 0.9866 - prc: 0.6290 - val_loss: 0.0944 - val_tp: 69.0000 - val_fp: 993.0000 - val_tn: 44498.0000 - val_fn: 9.0000 - val_accuracy: 0.9780 - val_precision: 0.0650 - val_recall: 0.8846 - val_auc: 0.9679 - val_prc: 0.6961\n",
      "Epoch 34/100\n",
      "90/90 [==============================] - 1s 6ms/step - loss: 0.1330 - tp: 291.0000 - fp: 4962.0000 - tn: 176998.0000 - fn: 25.0000 - accuracy: 0.9726 - precision: 0.0554 - recall: 0.9209 - auc: 0.9878 - prc: 0.6378 - val_loss: 0.0908 - val_tp: 69.0000 - val_fp: 960.0000 - val_tn: 44531.0000 - val_fn: 9.0000 - val_accuracy: 0.9787 - val_precision: 0.0671 - val_recall: 0.8846 - val_auc: 0.9689 - val_prc: 0.6964\n",
      "Epoch 35/100\n",
      "90/90 [==============================] - 1s 6ms/step - loss: 0.1442 - tp: 292.0000 - fp: 4851.0000 - tn: 177109.0000 - fn: 24.0000 - accuracy: 0.9733 - precision: 0.0568 - recall: 0.9241 - auc: 0.9840 - prc: 0.6406 - val_loss: 0.0905 - val_tp: 69.0000 - val_fp: 928.0000 - val_tn: 44563.0000 - val_fn: 9.0000 - val_accuracy: 0.9794 - val_precision: 0.0692 - val_recall: 0.8846 - val_auc: 0.9688 - val_prc: 0.6967\n",
      "Epoch 36/100\n",
      "90/90 [==============================] - 1s 6ms/step - loss: 0.1334 - tp: 293.0000 - fp: 4786.0000 - tn: 177174.0000 - fn: 23.0000 - accuracy: 0.9736 - precision: 0.0577 - recall: 0.9272 - auc: 0.9882 - prc: 0.6383 - val_loss: 0.0893 - val_tp: 69.0000 - val_fp: 909.0000 - val_tn: 44582.0000 - val_fn: 9.0000 - val_accuracy: 0.9799 - val_precision: 0.0706 - val_recall: 0.8846 - val_auc: 0.9675 - val_prc: 0.7056\n",
      "Epoch 37/100\n",
      "90/90 [==============================] - 1s 7ms/step - loss: 0.1373 - tp: 292.0000 - fp: 4898.0000 - tn: 177062.0000 - fn: 24.0000 - accuracy: 0.9730 - precision: 0.0563 - recall: 0.9241 - auc: 0.9867 - prc: 0.6386 - val_loss: 0.0883 - val_tp: 69.0000 - val_fp: 881.0000 - val_tn: 44610.0000 - val_fn: 9.0000 - val_accuracy: 0.9805 - val_precision: 0.0726 - val_recall: 0.8846 - val_auc: 0.9676 - val_prc: 0.6972\n",
      "Epoch 38/100\n",
      "90/90 [==============================] - 1s 6ms/step - loss: 0.1272 - tp: 294.0000 - fp: 4748.0000 - tn: 177212.0000 - fn: 22.0000 - accuracy: 0.9738 - precision: 0.0583 - recall: 0.9304 - auc: 0.9898 - prc: 0.6691 - val_loss: 0.0875 - val_tp: 69.0000 - val_fp: 917.0000 - val_tn: 44574.0000 - val_fn: 9.0000 - val_accuracy: 0.9797 - val_precision: 0.0700 - val_recall: 0.8846 - val_auc: 0.9680 - val_prc: 0.6974\n",
      "Epoch 39/100\n",
      "90/90 [==============================] - 1s 6ms/step - loss: 0.1313 - tp: 291.0000 - fp: 4908.0000 - tn: 177052.0000 - fn: 25.0000 - accuracy: 0.9729 - precision: 0.0560 - recall: 0.9209 - auc: 0.9885 - prc: 0.6476 - val_loss: 0.0853 - val_tp: 69.0000 - val_fp: 881.0000 - val_tn: 44610.0000 - val_fn: 9.0000 - val_accuracy: 0.9805 - val_precision: 0.0726 - val_recall: 0.8846 - val_auc: 0.9660 - val_prc: 0.6975\n",
      "Epoch 40/100\n",
      "90/90 [==============================] - 1s 6ms/step - loss: 0.1221 - tp: 293.0000 - fp: 4756.0000 - tn: 177204.0000 - fn: 23.0000 - accuracy: 0.9738 - precision: 0.0580 - recall: 0.9272 - auc: 0.9907 - prc: 0.6578 - val_loss: 0.0826 - val_tp: 69.0000 - val_fp: 848.0000 - val_tn: 44643.0000 - val_fn: 9.0000 - val_accuracy: 0.9812 - val_precision: 0.0752 - val_recall: 0.8846 - val_auc: 0.9664 - val_prc: 0.6979\n",
      "Epoch 41/100\n",
      "90/90 [==============================] - 1s 7ms/step - loss: 0.1425 - tp: 290.0000 - fp: 4726.0000 - tn: 177234.0000 - fn: 26.0000 - accuracy: 0.9739 - precision: 0.0578 - recall: 0.9177 - auc: 0.9859 - prc: 0.6476 - val_loss: 0.0850 - val_tp: 69.0000 - val_fp: 911.0000 - val_tn: 44580.0000 - val_fn: 9.0000 - val_accuracy: 0.9798 - val_precision: 0.0704 - val_recall: 0.8846 - val_auc: 0.9662 - val_prc: 0.6980\n",
      "Epoch 42/100\n",
      "90/90 [==============================] - 1s 7ms/step - loss: 0.1266 - tp: 289.0000 - fp: 4662.0000 - tn: 177298.0000 - fn: 27.0000 - accuracy: 0.9743 - precision: 0.0584 - recall: 0.9146 - auc: 0.9906 - prc: 0.6353 - val_loss: 0.0848 - val_tp: 69.0000 - val_fp: 925.0000 - val_tn: 44566.0000 - val_fn: 9.0000 - val_accuracy: 0.9795 - val_precision: 0.0694 - val_recall: 0.8846 - val_auc: 0.9664 - val_prc: 0.6735\n",
      "Epoch 43/100\n",
      "90/90 [==============================] - 1s 6ms/step - loss: 0.1327 - tp: 289.0000 - fp: 4539.0000 - tn: 177421.0000 - fn: 27.0000 - accuracy: 0.9750 - precision: 0.0599 - recall: 0.9146 - auc: 0.9882 - prc: 0.6644 - val_loss: 0.0822 - val_tp: 69.0000 - val_fp: 868.0000 - val_tn: 44623.0000 - val_fn: 9.0000 - val_accuracy: 0.9808 - val_precision: 0.0736 - val_recall: 0.8846 - val_auc: 0.9661 - val_prc: 0.6982\n",
      "Epoch 44/100\n",
      "90/90 [==============================] - 1s 7ms/step - loss: 0.1438 - tp: 288.0000 - fp: 4670.0000 - tn: 177290.0000 - fn: 28.0000 - accuracy: 0.9742 - precision: 0.0581 - recall: 0.9114 - auc: 0.9852 - prc: 0.6182 - val_loss: 0.0844 - val_tp: 69.0000 - val_fp: 902.0000 - val_tn: 44589.0000 - val_fn: 9.0000 - val_accuracy: 0.9800 - val_precision: 0.0711 - val_recall: 0.8846 - val_auc: 0.9663 - val_prc: 0.6653\n",
      "Restoring model weights from the end of the best epoch.\n",
      "Epoch 00044: early stopping\n"
     ]
    }
   ],
   "source": [
    "weighted_model = make_model()\n",
    "weighted_model.load_weights(initial_weights)\n",
    "\n",
    "weighted_history = weighted_model.fit(\n",
    "    train_features,\n",
    "    train_labels,\n",
    "    batch_size=BATCH_SIZE,\n",
    "    epochs=EPOCHS,\n",
    "    callbacks = [early_stopping],\n",
    "    validation_data=(val_features, val_labels),\n",
    "    # 使用 class_weights 会改变损失范围。这可能会影响训练的稳定性，具体取决于优化器\n",
    "    class_weight=class_weight)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 查看训练历史记录\n",
    "plot_metrics(weighted_history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss :  0.0886046513915062\n",
      "tp :  89.0\n",
      "fp :  1128.0\n",
      "tn :  55736.0\n",
      "fn :  9.0\n",
      "accuracy :  0.9800392985343933\n",
      "precision :  0.07313065230846405\n",
      "recall :  0.9081632494926453\n",
      "auc :  0.9855610132217407\n",
      "prc :  0.6803525686264038\n",
      "\n",
      "Legitimate Transactions Detected (True Negatives):  55736\n",
      "Legitimate Transactions Incorrectly Detected (False Positives):  1128\n",
      "Fraudulent Transactions Missed (False Negatives):  9\n",
      "Fraudulent Transactions Detected (True Positives):  89\n",
      "Total Fraudulent Transactions:  98\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 评估指标\n",
    "train_predictions_weighted = weighted_model.predict(train_features, batch_size=BATCH_SIZE)\n",
    "test_predictions_weighted = weighted_model.predict(test_features, batch_size=BATCH_SIZE)\n",
    "\n",
    "weighted_results = weighted_model.evaluate(test_features, test_labels,\n",
    "                                           batch_size=BATCH_SIZE, verbose=0)\n",
    "for name, value in zip(weighted_model.metrics_names, weighted_results):\n",
    "  print(name, ': ', value)\n",
    "print()\n",
    "\n",
    "plot_cm(test_labels, test_predictions_weighted)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在这里，我们可以看到，使用类权重时，由于存在更多假正例，准确率和精确率较低，但是相反，由于模型也找到了更多真正例，召回率和 AUC 较高。尽管准确率较低，但是此模型具有较高的召回率（且识别出了更多欺诈交易）。当然，两种类型的错误都有代价（客户也不希望因将过多合法交易标记为欺诈来打扰客户）。请在应用时认真权衡这些不同类型的错误。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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eqdNJVlaW3n77bb333nu64oorJOWPhn3wwQd66qmnNG/ePMXFed596r333lOvXr3UpEmTEs8dHBxc6FgAAICAtexqKT1Jik6Qpu40uhog4Jk6qOXl5cnhcCgsLMxjfXh4uDZt2lRo/2PHjumjjz7SsmXLSj33nj17lJCQoLCwMHXr1k0LFy5UgwYNit0/JydHOTk57uW0tDRJkt1ul91u9/YtFenM8ed7HniPPjcG/W4M+t0YVabf8+yy/fnS6XLKYfL3U2X63QDBcskiySWX8srYf/S7Meh3Y/irvy0ul8vll5bKqXv37goJCdHy5ctVt25dvf766xo9erSaNWumXbt2eey7aNEiPfDAA0pKSioU7s728ccfKyMjQy1bttSRI0c0d+5cHT58WD///LOio6OLPGbOnDmaO3duofXLly9XRAS3sQUAFMPlUpukN1U7/RejKyknl6pl/SZJSo5qpc3N7zK4HlSUfj/frnD7SWXZquvTCx81uhzAtDIzMzVq1CilpqYqJiamwtoxfVBLTEzUTTfdpM8//1xWq1WdOnVSixYttG3bNu3c6Tks36pVK/Xt21ePP/54mdo4deqUGjZsqMWLF+vmm28ucp+iRtTq16+v5OTk8/4B2e12rVmzRn379pXNZiv9AJw3+twY9Lsx6HdjnOn3/m1rKOy/g4wuxyecTa6SY+SbRpdRIn7fyy/4sXaypB+RKzpeeZO2l+lY+t0Y9LsxUlJSFB8fX+FBzdRTHyWpadOm2rhxo06fPq20tDTFx8dr+PDhha5B++KLL7Rr1y698cYbZW6jWrVqatGihfbu3VvsPqGhoQoNDS203maz+ewfhi/PBe/Q58ag341Bvxsj2J5+1pJFspj+hstFi6ytoEv/qaBK8jvE73t5WP78X0u5+45+Nwb97l/+6mvTB7UzIiMjFRkZqZMnT+qTTz7RokWLPLa/8MIL6ty5szp06FDmc2dkZCgxMVH/+Mc/fFUuAACF9bpL6nmn0VUAACoB0/9Z75NPPtHq1au1f/9+rVmzRr169VKrVq00duxY9z5paWl66623NG7cuCLP0bt3by1dutS9PG3aNG3cuFEHDhzQl19+qSFDhshqtWrkyJEV/n4AAAAAoDSmH1FLTU3VzJkzdejQIdWoUUNDhw7V/PnzPYYcV6xYIZfLVWzQSkxMVHJysnv50KFDGjlypFJSUlS7dm316NFDX331lWrXrl3h7wcAAAAASmP6oDZs2DANGzasxH3Gjx+v8ePHF7v9wIEDHssrVqzwRWkAAAAAUCFMP/URAAAAAAKN6UfUAAAA4Afj10tOhxRkNboSACKoAQAAQJKi44yuAMBZmPoIAAAAACZDUAMAAAAAk2HqIwCg6nE6pff+Je1da1gJwZL65+TIusOwEoCy2fqSlHtaComUuowtfX8AFYqgBgCoepK+l3583dASLJLCJCnvrJWh0cYUA3hj4yIpPUmKTiCoASZAUAMAVD25GQWvQ2Ol8Gp+L8ElKTMzUxEREbJIUp3W0oXX+70OAEDlRFADAFRtl4yTes/2e7N5drvWrlqlgQMHymaz+b19AEDlxs1EAAAAAMBkCGoAAAAAYDIENQAAAAAwGYIaAAAAAJgMNxMBAEn6ZaW0foGUk1H09s6jpStneK57uLV3577uWanx5QXL+7+Q3hnv3bFTd3oub3hA2ras9OMaXSYNfd5jlfW/10on9pV+bM87PW/NnX5UerZX6cdJ0uj3pVrNC5Z/ekta48WNPKLqSBM2eq774HZp96elH9tuqNRvnue6d/9Z8PrrZ6QfirlV/6BHpJYDCpaTvpdeH1V6m5I08RvP2+1/uVTa8oR7MVgu9cvOVvDefyv/Zv1/iu8gjVrhea7lI6QjP5beZrdbpe4TC5Zz0qWll3hX78jlUsJFBcu7Vksf3lH6cSGR0m1bPdd9eo+0/e3Sj23RTxr8qOe6Z3pKGcdLP7bvfVL7vxUsJ++Rll1d6mHBcim04UzPlVtfyr/1fGlqNpXGfOi57u1x0oHNpR9bBf4bofQk79oE4BcENQCQ8kNa8u7it2enFV7n7YcaR07h5fJ+IMpO8+7YzJRCqyyn//Du2NzTnstOh/f1OvM8l+2Z5X+vWSe9OzbrVBHrThS8zs3wvF3/2fKyPJcddu/rdbk8l3PSPY61SAqXJPs5x8XWK3yuzGTv2s1JL1yD17+H5xSSl+XdsSFFPPst65SXP5uThddlHPfuWHum57Izz6vjLJIsLqfnytzT3rUZFlN4XWaKd8dWgf9GyBaR3++hUeVrG4BPEdQAQCoYSbMESVFxhbcX9QEuOsG7c1tDCy97e2xRdXhzbETNQqtckbVlOfeDflFCIj2Xg6ze1xt0zv+t2CK8OzaqTuF14dW9O7aoZ6SF15Dsh/Nfh0RJoUX8/CQpONxz2Wrz/r1aLJ7LodEex7rkUnZ2tsLCwmQ5e0Qtolbhc0XU8q7dcx+YbbGU4ffwnEcEBId7d+y5vw9Sfp979bOpXnhdUT/rotgiPJeDgr1q0yWXXJZzruwIifSu3sjahddF1PTu2Crw3wjV6yxlHJN63V2+tgH4lMXlOvdPgvBGWlqaYmNjlZqaqpiYYj4AeMlut2sVz9rxK/rcGKbu94db5/8VOjqh8FSisnI6C48sGcieZ9fHH6/WX/4yQLZgk/V7RTnwufTfofmvL59qyHPUTP37XoXR78ag341BvxsjJSVFtWrV8kkOKAkjagAg5V+vkZlS9F+ZyyJxff41LZnJvqnLB2ySrpYkLy6BAgAA5kBQAwCp8EX15fXdK6YKaVDRUw0BADA5ghoA+JIjt+B1vS5ScGjx+/qJ0+XUiRMnVKNGDQWde+1OVVezmdRhhNFVAABQZgQ1AKgoI16Toou4MYmfOex2bf7zGoYgrmEAAKBSCLA/rQIAAACA+TGiBgCS9PIg6fQf+bfnPveBtwAAAH5GUAMASUpJzL89f1EPrQUAAPAzpj4CAAAAgMkQ1AAAAADAZAhqAAAAAGAyBDUAAAAAMBluJgLA3E6nSDmpFd+OM6/g+4l95T9P7mnf1AMAAAIaQQ2AeX39rLT635LL6b82Tx+XHrvIf+0BAAAUgamPAMxr+1v+DWm+FBIthcYYXQUAAKikGFEDYF4uR8HrdsMqtq2dH0h5WVJwuNR68PmdKyhYane9FBLhm9oAAEDAIagBqAQs0tDnKraJrS/lX18WEil1GVuxbQEAAJSCoAYAEuEMAACYCteoAQAAAIDJENQAAAAAwGSY+ggAkpR+VHI6pCCrFB1ndDUAACDAEdQAQJKe7SWlJ0nRCdLUnUZXAwAAAhxTHwEAAADAZAhqAAAAAGAyTH0E4H8ul5T0vZSTUfJ+Oen+qQcAAMBkCGoA/C7o42nS98uMLgMAAMC0mPoIwO+CEteV7YBazSumEAAAAJNiRA2AcWyRUtfxJe8THCZdeL1/6gEAADAJghoA44RGS33mGF0FAACA6TD1EQAAAABMhqAGAAAAACbD1EcAkKTR70vOPCmI/ywCAADj8YkEACTuLAkAAEyFqY8AAAAAYDIENQAAAAAwGaY+AoAk/fSWZM+UbBFS+78ZXQ0AAAhwBDUA3jm6XTr2y3mdwuJw6IITP0r20+dXyy8rpfULpJyM4vdp0U8a/Kjnumd6ShnHi94/PSn/e3QCQQ0AABiOoAagdL9tkV4acN6nCZbU+fyryQ9pybtL3ifrZOF1GccLAllxQqPKXxcAAICPENQAlO7w1oo5b3yH8h13ZiTNEiRFxRW9T3j1wuui6pR83tAoqdfd5asJAADAhwhqAMrmon+UO2A5HE79/MvPurDthbKGx0it/lq+GtoNlbJOSeHVpH7zvD9uwsbytQcAAOBnBDUAZdOsj9T22nId6rTbdeD4KrXpMlBWm638NZQlnAEAAFRC3J4fAAAAAEyGoAYAAAAAJkNQAwAAAACT4Ro1AJXP412k9KNSdJx0WwXdkRIAAMBAjKgBqHxyT0u56fnfAQAAqiCCGgAAAACYDEENAAAAAEyGa9QAFO+P3dKuVdKBL4yuBAAAIKAQ1AAULS9HemmAlJlidCUAAAABh6mPAIp2OrlwSLOGSPUvMaYeAACAAMKIGoDS1e8q9bhDSugkRdc1uhoAAIAqj6AGoHTRcVLLvxhdBQAAQMBg6iMAAAAAmAwjagAqn0GPSHlZUnC40ZUAAABUCIIagMqn5QCjKwAAAKhQTH0EAAAAAJMhqAEAAACAyTD1EUDlk/S95LBLVpuUcJHR1QAAAPgcQQ1A5fP6KCk9SYpOkKbuNLoaAAAAnyOoAZXF4W3ST29Kjlz/tJd72j/tAAAAoBCCGlAZOJ3Sihuk9CPGtG/hclYAAAB/4tMXUBk4co0Naa0GGdM2AABAgGJEDahs4jtKVz/uv/ai6kjRcf5rDwAAAAQ1oNIJjZbi2xtdBQAAACoQUx8BAAAAwGQIagAAAABgMqYPaunp6Zo8ebIaNmyo8PBwde/eXd9++617+5gxY2SxWDy+BgwYUOp5n3jiCTVq1EhhYWHq2rWrvvnmm4p8GwAAAADgNdMHtXHjxmnNmjV69dVXtX37dvXr1099+vTR4cOH3fsMGDBAR44ccX+9/vrrJZ7zjTfe0JQpU3Tvvffqu+++U4cOHdS/f38dP368ot8OAAAAAJTK1EEtKytLb7/9thYtWqQrrrhCzZo105w5c9SsWTM99dRT7v1CQ0MVFxfn/qpevXqJ5128eLFuueUWjR07Vm3atNHTTz+tiIgIvfjiixX9lgD4wsRvpBm/538HAACogkx918e8vDw5HA6FhYV5rA8PD9emTZvcyxs2bFCdOnVUvXp1XXXVVZo3b55q1qxZ5Dlzc3O1bds2zZw5070uKChIffr00ZYtW4qtJScnRzk5Oe7ltLQ0SZLdbpfdbi/X+zvjzPHnex54r9L1eZ5dtj9fOl1OOSpL3efwWb8HnfXfhEraF/5U6X7fqwj63Rj0uzHod2PQ78bwV39bXC6Xyy8tlVP37t0VEhKi5cuXq27dunr99dc1evRoNWvWTLt27dKKFSsUERGhxo0bKzExUXfddZeioqK0ZcsWWa3WQudLSkpSvXr19OWXX6pbt27u9Xfeeac2btyor7/+usg65syZo7lz5xZav3z5ckVERPjuDQNFCHLmavCP4yRJf0S11pfNZ5ZyBAAAACpCZmamRo0apdTUVMXExFRYO6YeUZOkV199VTfddJPq1asnq9WqTp06aeTIkdq2bZskacSIEe5927Vrp/bt26tp06basGGDevfu7bM6Zs6cqSlTpriX09LSVL9+ffXr1++8f0B2u11r1qxR3759ZbPZSj8A5810fZ68W9YvH5WyThS93el0v6xZs4YGDhzop8J8y3T9HiDod2PQ78ag341BvxuDfjdGSkqKX9oxfVBr2rSpNm7cqNOnTystLU3x8fEaPny4mjRpUuT+TZo0Ua1atbR3794ig1qtWrVktVp17Ngxj/XHjh1TXFxcsXWEhoYqNDS00Hqbzeazfxi+PBe8Y5o+3zBP2rXKq12DrCEKMkPN5+G8+/3LpVJOev7Dv7tP9F1hVZxpft8DDP1uDPrdGPS7Meh3//JXX5s+qJ0RGRmpyMhInTx5Up988okWLVpU5H6HDh1SSkqK4uPji9weEhKizp07a926dbr22mslSU6nU+vWrdPEiXzgg0EyjpW+jyTZIqSL/u7btn9ZKa1fIOVk5C8PekRqedYjLpK+l14f5d25Jn6TH57O+HKptOUJ92KwXOqXna3gvf+WZCnYL76DNGqF57mWj5CO/Fh0OxlHJZdTik4gqAEAgCrJ9EHtk08+kcvlUsuWLbV3715Nnz5drVq10tixY5WRkaG5c+dq6NChiouLU2Jiou688041a9ZM/fv3d5+jd+/eGjJkiDuITZkyRaNHj1aXLl10ySWXaMmSJTp9+rTGjh1r1NsECty5v/httvD8L19av0BK3l2wnJflud1hl9KTvDvXuZe85qR7HGuRFC5J516DG1uv8Lkyk0tvNzTKu7oAAAAqGdMHtdTUVM2cOVOHDh1SjRo1NHToUM2fP182m015eXn66aeftGzZMp06dUoJCQnq16+f7r//fo9piomJiUpOTnYvDx8+XH/88Ydmz56to0ePqmPHjlq9erXq1q1rxFsEPEXU8G97Z0bSLEFSVJwUfE4QtNryR668YbF4LodGexzrkkvZ2dkKCwuT5ewRtYhahc8VUavkdkOjpF53e1cXAABAJWP6oDZs2DANGzasyG3h4eH65JNPSj3HgQMHCq2bOHEiUx2Bs0XFSVN3Fl6fcFHR673RfaLH1MQ8u12frlqlgQMHlj6/+9ypkAAAAAHE1A+8BgAAAIBARFADAAAAAJMhqAEAAACAyRDUAAAAAMBkCGpAoAuJlEKi878DAADAFEx/10cAFey2rUZXAAAAgHMQ1IDiOJ3S6hnSb5srvq2UxIpvAwAAAJUGQQ0ozsEt0jfP+LfNcx82DQAAgIBEUAOKk32q4HVQsBRUygOaz1dwqNTjjoptAwAAAJUCQQ3wRq+7pcunGF1Fxfj0HinrlBReTeo3z+hqAAAAIO76CGD729L3r+Z/BwAAgCkQ1AAAAADAZAhqAAAAAGAyBDUAAAAAMBmCGgAAAACYDEENAAAAAEyGoAYAAAAAJkNQAwAAAACT4YHXwBm5p6U3/iEd3pa/7LAbWw8AAAACFkENOGP3J1LiuqK3hcX4txZ/atFPyjophVc3uhIAAAD8iaAGnGHPKngdWVsKi81/Xae11PY6Y2ryh8GPGl0BAAAAzkFQA4rS6y6py01GVwEAAIAAxc1EAAAAAMBkCGoAAAAAYDJMfQQC3TM9pYzjUlQdacJGo6sBAACACGoAMo5L6UlGVwEAAICzMPURAAAAAEyGoAYAAAAAJkNQAwAAAACTIagBZzjzjK4AAAAAkERQA/LtXSd9MMnoKgAAAABJBDUg3w+veS5H1jamDgAAAEAENSCfI7fgdfsRUvP+xtUCAACAgEdQA87VZ44UHGJ0FQAAAAhgPPAaCAS/rJTWL5ByMgrWjX5fqtVc6nufZM+UbBHG1QcAAAAPBDUgEKxfICXv9lx35i6X7f/m/3oAAABQIoIaEAjOjKRZgqSouPzXQfzzBwAAMCs+qQGBJCpOmrrT6CoAAABQCm4mAgAAAAAmQ1ADAAAAAJMhqAEAAACAyXCNGqqejD+krBMl75OXp6jsw/l3QgwOlnLS/VMbAAAA4AWCGqqWH16X3rtVcjlK3M0mqbckBcp9Ncavl5wOKchqdCUAAADwAkENVcsvK0sNaSUKiZbCYn1Xj1lExxldAQAAAMqAoIaqxeUseH3h9VJwaJG7OZ1OHTp0SBdccIGCgv68VDPImn9MSIQfCgUAAACKR1BD1fXXh6Tw6kVuctjt+n7VKsUPHKggm83PhQEAAAAlI6gBgWDrS1LuaSkkUuoy1uhqAAAAUAqCGhAINi6S0pOk6ASCGgAAQCXAc9QAAAAAwGQIagAAAABgMgQ1AAAAADAZghoAAAAAmAxBDQAAAABMhrs+onI6+rOUcazw+sxk/9cCAAAA+BhBDZXPliekT+4yugoAAACgwjD1EZVP4vrS94mOl0KiK74WAAAAoAIwoobK7ZIJUng1z3VBNqnNNZKVX2+3mk2lsBgpsrbRlQAAAMALfJJF5dZrphRe3egqzG/Mh0ZXAAAAgDJg6iMAAAAAmAxBDQAAAABMhqAGAAAAACbDNWpAIHh7nJSZIkXUlIY+b3Q1AAAAKAVBDQgEBzZL6UlSdILRlQAAAMALTH0EAAAAAJMhqAEAAACAyRDUULnk5Up71xhdBQAAAFChCGqoXNbOMboCAAAAoMIR1FC5JH1X8LpaAyk01rhaAAAAgApCUEPldfMaKYhfYQAAAFQ9fMpF5RVRy+gKAAAAgApBUAMAAAAAk+GB10Ag6Dxayk6TwmKMrgQAAABeIKgBgeDKGUZXAAAAgDIgqAFm8ctKaf0CKSej6O3XPSs1vrxgef8X0jvjvTv31J3nXx8AAAD8hqAGmMX6BVLy7uK3O3IKL6cnVWxNAAAAMARBDTCLMyNpliApKq7wdmto4eXohIqvCwAAAH5HUAPMJirOu6mKjS9nSiMAAEAVRVCD/6Qekrb/T7Jnlf8cp373XT0AAACASRHU4D9vjZEOfWt0FebF6BgAAAD+xAOv4T9/lHCjjLJqdLlk5e8MAAAAqJr4pAv/i6knXf1Y+Y+3hkoNLvVdPQAAAIDJENTgfyGRUrM+RlcBAAAAmBZBDTCLDQ9I2WlSWIx05QyjqwEAAICBTH+NWnp6uiZPnqyGDRsqPDxc3bt317ff5t+Qwm6369///rfatWunyMhIJSQk6MYbb1RSUskPAZ4zZ44sFovHV6tWrfzxdoDibVsmffVE/ncAAAAENNOPqI0bN04///yzXn31VSUkJOi///2v+vTpox07digqKkrfffedZs2apQ4dOujkyZO6/fbbdfXVV2vr1q0lnrdt27Zau3atezk42PRdAQAAACBAmDqdZGVl6e2339Z7772nK664QlL+aNgHH3ygp556SvPmzdOaNWs8jlm6dKkuueQSHTx4UA0aNCj23MHBwYqLi6vQ+gEAAACgPEwd1PLy8uRwOBQWFuaxPjw8XJs2bSrymNTUVFksFlWrVq3Ec+/Zs0cJCQkKCwtTt27dtHDhwhKDXU5OjnJyctzLaWlpkvKnX9rtdi/fUdHOHH++5zG7YLlkkeRyuZRn8Hs1Y5+7+0fG909FMWO/BwL63Rj0uzHod2PQ78ag343hr/62uFwul19aKqfu3bsrJCREy5cvV926dfX6669r9OjRatasmXbt2uWxb3Z2ti677DK1atVKr732WrHn/Pjjj5WRkaGWLVvqyJEjmjt3rg4fPqyff/5Z0dHRRR4zZ84czZ07t9D65cuXKyIi4vzeZIAY+NP/yebIVHpovD5r8x+jyzGdfj/frnD7SWXZquvTCx81uhwAAAAUITMzU6NGjVJqaqpiYmIqrB3TB7XExETddNNN+vzzz2W1WtWpUye1aNFC27Zt086dO9372e12DR06VIcOHdKGDRvK1GmnTp1Sw4YNtXjxYt18881F7lPUiFr9+vWVnJx83j8gu92uNWvWqG/fvrLZbOd1LjMLfqiJLDlpctVsrrz/22JoLWbs8+DH2smSfkSu6HjlTdpudDkVwoz9Hgjod2PQ78ag341BvxuDfjdGSkqK4uPjKzyomXrqoyQ1bdpUGzdu1OnTp5WWlqb4+HgNHz5cTZo0ce9jt9s1bNgw/fbbb/rss8/K3GHVqlVTixYttHfv3mL3CQ0NVWhoaKH1NpvNZ/8wfHkuUzixX/r2eSn7VP6yPUuSZLFYTPM+zdXnlj//1zz9U1HM1e+Bg343Bv1uDPrdGPS7Meh3//JXX5s+qJ0RGRmpyMhInTx5Up988okWLVokqSCk7dmzR+vXr1fNmjXLfO6MjAwlJibqH//4h6/LDmyrZ0i7VxdebzH9UyEAAAAAQ5n+E/Mnn3yi1atXa//+/VqzZo169eqlVq1aaezYsbLb7br++uu1detWvfbaa3I4HDp69KiOHj2q3Nxc9zl69+6tpUuXupenTZumjRs36sCBA/ryyy81ZMgQWa1WjRw50oi3WHWd+r3o9RcO9W8dAAAAQCVj+hG11NRUzZw5U4cOHVKNGjU0dOhQzZ8/XzabTQcOHND7778vSerYsaPHcevXr9eVV14pKf86t+TkZPe2Q4cOaeTIkUpJSVHt2rXVo0cPffXVV6pdu7a/3lZgsYZKEz7Pfx0aLcXWM7Yes2p0mZSZIkWUfVQYAAAAVYvpg9qwYcM0bNiwIrc1atRI3twL5cCBAx7LK1as8EVp8JYlSKrTyugqzG/o80ZXAAAAAJMw/dRHAAAAAAg0BDUAAAAAMBmCGgAAAACYjOmvUQMCxsuDpNN/SJG1pTEfGl0NAAAADERQA8wiJVFKT5Ky04yuBAAAAAZj6iMAAAAAmAxBDQAAAABMhqCGipG8Vzr+i9FVAAAAAJUSQQ0V4/NFBa8tFuPqAAAAACohghoqRsbxgtedxxhWBgAAAFAZEdRQ8XrdZXQFAAAAQKVCUAMAAAAAkyGoAQAAAIDJ8MBrwCx63inlnpZCIo2uBAAAAAYjqAFm0WWs0RUAAADAJJj6CAAAAAAmQ1ADAAAAAJMhqAFG+2WltPRiaVFT6bctUvpRoysCAACAwby6Rq1Tp05lOqnFYtH777+vevXqlasoIKCsXyAl785//dIAqVYLaeK3xtYEAAAAQ3kV1H744QdNnTpVUVFRpe7rcrn0wAMPKCcn57yLQyWR9L30yT3S6eMF61IPGVdPZZOTkf/dEiTVbCb1utvYegAAAGA4r+/6OH36dNWpU8erfR9++OFyF4RKaNMS6bdNRW8LCpaCbH4tp9KKimMkDQAAAJK8DGr79+9X7dq1vT7pjh07lJCQUO6iUMnkpBW8Do2RZMl/bbVJXSdItjBDygIAAAAqK6+CWsOGDct00vr165erGFQBd/wihcUYXQUAAABQqZX7gdd5eXl65plntGHDBjkcDl122WW69dZbFRbG6AkAAAAAnI9yB7VJkyZp9+7duu6662S32/XKK69o69atev31131ZHwAAAAAEHK+D2sqVKzVkyBD38qeffqpdu3bJarVKkvr3769LL73U9xUCAAAAQIDxOqi9+OKLWrZsmZ588kklJCSoU6dO+r//+z8NHTpUdrtdzz33nC6++OKKrBWomka/Lznz8u+QCQAAAEgK8nbHDz74QCNHjtSVV16pxx9/XM8++6xiYmJ09913a9asWapfv76WL19ekbUCVVOt5lKd1vnfAQAAAJXxGrXhw4erf//+uvPOO9W/f389/fTTPDMNAAAAAHzM6xG1M6pVq6Znn31WDz74oG688UZNnz5d2dnZFVEbAAAAAAQkr4PawYMHNWzYMLVr10433HCDmjdvrm3btikiIkIdOnTQxx9/XJF1AlXXT29J25blfwcAAABUhqB24403KigoSA8++KDq1KmjCRMmKCQkRHPnztW7776rhQsXatiwYRVZK8xm7zppSTtp3wajK6nc1syWPpiU/x0AAABQGa5R27p1q3788Uc1bdpU/fv3V+PGjd3bWrdurc8//1zPPvtshRQJk/rqKenUwYLl4HApONS4egAAAIAqwuug1rlzZ82ePVujR4/W2rVr1a5du0L7jB8/3qfFweTyzro2Ma6ddPE4ghoAAADgA15PfXzllVeUk5OjO+64Q4cPH9YzzzxTkXWhsrl5rdR5jNFVAAAAAFWC1yNqDRs21P/+97+KrAUAAAAAIC9H1NLS0sp00vT09HIVAwAAAADwMqhVr15dx48f9/qk9erV0759+8pdFAAAAAAEMq+mPrpcLj3//POKiory6qR2u/28igIAAACAQOZVUGvQoIGee+45r08aFxcnm81W7qIAAAAAIJB5FdQOHDhQwWUAASyqjud3AAAABDyv7/oIoIJM2Gh0BQAAADAZghrKJi9Hsmfmv3ZwLSIAAABQEQhq8N6etdL/bpJyUo2uBAAAAKjSvLo9PyBJ+vH1okNaeHXJys1jAAAAAF9hRA3ec5411bFOWykmQbKGSJ1HS0FW4+qq7D64Xco6mR94Bz9qdDUAAAAwgTIHtdWrVysqKko9evSQJD3xxBN67rnn1KZNGz3xxBOqXr26z4uECd3wlhRbz+gqqobdn0rpSVJ0gtGVAAAAwCTKPPVx+vTpSktLkyRt375dU6dO1cCBA7V//35NmTLF5wUCAAAAQKAp84ja/v371aZNG0nS22+/rUGDBmnBggX67rvvNHDgQJ8XCAAAAACBpswjaiEhIcrMzL89+9q1a9WvXz9JUo0aNdwjbQAAAACA8ivziFqPHj00ZcoUXXbZZfrmm2/0xhtvSJJ2796tCy64wOcFAgAAAECgKfOI2tKlSxUcHKz//e9/euqpp1SvXv4NJT7++GMNGDDA5wUCAAAAQKAp84hagwYN9OGHHxZa/8gjj/ikIAAAAAAIdOV64HViYqLuuecejRw5UsePH5eUP6L2yy+/+LQ4AAAAAAhEZQ5qGzduVLt27fT111/rnXfeUUZGhiTpxx9/1L333uvzAmESWaekYwRxAAAAwB/KPPVxxowZmjdvnqZMmaLo6Gj3+quuukpLly71aXEwicPfSS8NlPKyynd80vfS66O823fiN1Jowe+VvlwqbXmi9OPiO0ijVniuWz5COvJjkbsHy6V+2dkK3vtvqdtEqfvEgo056dLSS7yrd+RyKeGiguVdq6UP7yj9uJBI6bat+a/bDc0PwuHVvGsTAAAAVV6Zg9r27du1fPnyQuvr1Kmj5ORknxQFk9nzqWdIC4mWwqt7f7zDLqUnebevy+W5nJPu3bGx9Qqvy0wu9liLpHBJsv/Zxrk1eFuvw+65nJfl3bEhZ4XRfvO8awsAAAABo8xBrVq1ajpy5IgaN27ssf7777933wESVYzLWfC6RhPp6selkAjvj7fapOgE7/a1WDyXQ6O9OzaiVtHrijnWJZeys7MVFhYmy9kjeGdq8LZeq81zOTjcu2NDIr07PwAAAAJSmYPaiBEj9O9//1tvvfWWLBaLnE6nNm/erGnTpunGG2+siBphJgMfkhr1KNsxCRdJU3eWr73u50xLLItzp0KeJc9u16erVmngwIGy2c4JW6HR5a+35YD8LwAAAOA8lDmoLViwQLfeeqvq168vh8OhNm3ayOFwaNSoUbrnnnsqokZUVrtW508FDA4nvAAAAABlUOagFhISoueee06zZs3Szz//rIyMDF100UVq3rx5RdSHyuzDO/Kv14pOIKgBAAAAZVDmoLZp0yb16NFDDRo0UIMGDSqiJgAAAAAIaGV+jtpVV12lxo0b66677tKOHTsqoiYAAAAACGhlDmpJSUmaOnWqNm7cqAsvvFAdO3bUgw8+qEOHDlVEfQAAAAAQcMoc1GrVqqWJEydq8+bNSkxM1N/+9jctW7ZMjRo10lVXXVURNQIAAABAQClzUDtb48aNNWPGDD3wwANq166dNm7c6Ku6AAAAACBglTuobd68Wf/6178UHx+vUaNG6cILL9RHH33ky9pgpNxMae9aafcnUspeo6sBAAAAAkqZ7/o4c+ZMrVixQklJSerbt68effRRXXPNNYqIiKiI+mAEp1N6tqeUvNvoSgAAAICAVOag9vnnn2v69OkaNmyYatWqVRE1wWinjxcd0ixBUq0W/q8HAAAACDBlDmqbN2+uiDpgVrVaSu3+lv+68RVStfreHxsSKYVE538HAAAA4DWvgtr777+vv/zlL7LZbHr//fdL3Pfqq6/2SWEwiVrNpZ7Ty3fsbVt9WwsAAAAQILwKatdee62OHj2qOnXq6Nprry12P4vFIofD4avaAAAAACAgeRXUnE5nka8BAAAAAL5X5tvzv/LKK8rJySm0Pjc3V6+88opPigIAAACAQFbmoDZ27FilpqYWWp+enq6xY8f6pChUEZ/eI703Mf87AAAAAK+VOai5XC5ZLJZC6w8dOqTY2FifFIUqYvvb0vev5n8HAAAA4DWvb89/0UUXyWKxyGKxqHfv3goOLjjU4XBo//79GjBgQIUUCQAAAACBxOugduZujz/88IP69++vqKgo97aQkBA1atRIQ4cO9XmBAAAAABBovA5q9957rySpUaNGGj58uMLCwiqsKBgo4w/pu1eNrgIAAAAIaF4HtTNGjx5dEXXADFwu6eW/Ssm7jK4EAAAACGheBbUaNWpo9+7dqlWrlqpXr17kzUTOOHHihM+Kg5/lZRcOaRd0MaYWAAAAIIB5FdQeeeQRRUdHu1+XFNRQhfxjpdT4SqOrAAAAAAKOV0Ht7OmOY8aMqahaYCaNLpeaXmV0FQAAAEBAKvNz1L777jtt377dvfzee+/p2muv1V133aXc3FyfFiflP0h78uTJatiwocLDw9W9e3d9++237u0ul0uzZ89WfHy8wsPD1adPH+3Zs6fU8z7xxBNq1KiRwsLC1LVrV33zzTc+rx0AAAAAyqPMQW3ChAnavXu3JGnfvn0aPny4IiIi9NZbb+nOO+/0eYHjxo3TmjVr9Oqrr2r79u3q16+f+vTpo8OHD0uSFi1apMcee0xPP/20vv76a0VGRqp///7Kzs4u9pxvvPGGpkyZonvvvVffffedOnTooP79++v48eM+rz+gtegntbkm/zsAAAAAr5U5qO3evVsdO3aUJL311lvq2bOnli9frpdffllvv/22T4vLysrS22+/rUWLFumKK65Qs2bNNGfOHDVr1kxPPfWUXC6XlixZonvuuUfXXHON2rdvr1deeUVJSUl69913iz3v4sWLdcstt2js2LFq06aNnn76aUVEROjFF1/0af0Bb/Cj0rBX8r8DAAAA8FqZb8/vcrnkdDolSWvXrtWgQYMkSfXr11dycrJPi8vLy5PD4Sj0zLbw8HBt2rRJ+/fv19GjR9WnTx/3ttjYWHXt2lVbtmzRiBEjCp0zNzdX27Zt08yZM93rgoKC1KdPH23ZsqXYWnJycpSTk+NeTktLkyTZ7XbZ7fZyv8cz5zj7u2Hsdtn+fOl0ueQwup4KZJo+DzD0uzHod2PQ78ag341BvxuDfjeGv/q7zEGtS5cumjdvnvr06aONGzfqqaeekiTt379fdevW9Wlx0dHR6tatm+6//361bt1adevW1euvv64tW7aoWbNmOnr0qCQVardu3brubedKTk6Ww+Eo8phff/212FoWLlyouXPnFlr/6aefKiIioqxvrUhr1qzxyXnKK8iZq8F/vk5JSdGXq1YZWo8/GN3ngYp+Nwb9bgz63Rj0uzHod2PQ7/6VmZnpl3bKHNSWLFmiG264Qe+++67uvvtuNWvWTJL0v//9T927d/d5ga+++qpuuukm1atXT1arVZ06ddLIkSO1bds2n7dVkpkzZ2rKlCnu5bS0NNWvX1/9+vVTTEzMeZ3bbrdrzZo16tu3r2w2W+kHVBR7lvRj/suaNWtq4MCBxtVSwUzT5wGGfjcG/W4M+t0Y9Lsx6Hdj0O/GSElJ8Us7ZQ5q7du397jr4xkPPvigrFarT4o6W9OmTbVx40adPn1aaWlpio+P1/Dhw9WkSRPFxcVJko4dO6b4+Hj3MceOHXNfR3euWrVqyWq16tixYx7rjx075j5fUUJDQxUaGlpovc1m89k/DF+eq3zy3K+CLBYFnW8tz/SUMo5LUXWkCRvPs7aKYXyfByb63Rj0uzHod2PQ78ag341Bv/uXv/q6zEHtjG3btmnnzp2SpDZt2qhTp04+K6ookZGRioyM1MmTJ/XJJ59o0aJFaty4seLi4rRu3Tp3MEtLS9PXX3+tf/7zn0WeJyQkRJ07d9a6det07bXXSpKcTqfWrVuniRMnVuh7MKXkPdLPb0t5OZLTx/NtM45L6Um+PScAAAAQAMoc1I4fP67hw4dr48aNqlatmiTp1KlT6tWrl1asWKHatWv7tMBPPvlELpdLLVu21N69ezV9+nS1atVKY8eOlcVi0eTJkzVv3jw1b95cjRs31qxZs5SQkOAOYZLUu3dvDRkyxB3EpkyZotGjR6tLly665JJLtGTJEp0+fVpjx471ae2m53JJr10vnTxgdCUAAAAAzlLmoHbbbbcpIyNDv/zyi1q3bi1J2rFjh0aPHq1Jkybp9ddf92mBqampmjlzpg4dOqQaNWpo6NChmj9/vnvI8c4779Tp06c1fvx4nTp1Sj169NDq1as97hSZmJjocUfK4cOH648//tDs2bN19OhRdezYUatXr/b5zVBMz5lXfEhr3tevpQAAAAAoUOagtnr1aq1du9Yd0qT8qY9PPPGE+vXz/YONhw0bpmHDhhW73WKx6L777tN9991X7D4HDhwotG7ixImBOdWxOHXaSgMW5r+OrC3VbWNsPQAAAEAAK3NQczqdRV5AZ7PZ3M9XQyUUFis16Xn+5/npLWnN7PzXGUU/IgEAAABAyYLKesBVV12l22+/XUlJBTeJOHz4sO644w717t3bp8XB5JL3SMd35n8/w56ZfwOR9CTJ9WdwD40ypj4AAACgkirziNrSpUt19dVXq1GjRqpfv74k6ffff9eFF16o//73vz4vECa27Or8QBadIE3NvwOobBH5y2eERkm97jamPgAAAKCSKnNQq1+/vr777jutXbtWv/76qySpdevW6tOnj8+LQyXU/m/5XwAAAADKrVzPUbNYLOrbt6/69uXOgAAAAADga2W+Rk2S1q1bp0GDBqlp06Zq2rSpBg0apLVr1/q6NgAAAAAISGUOak8++aQGDBig6Oho3X777br99tsVExOjgQMH6oknnqiIGgEAAAAgoJR56uOCBQv0yCOPeDyDbNKkSbrsssu0YMEC3XrrrT4tEAAAAAACTZlH1E6dOqUBAwYUWt+vXz+lpqb6pCj4icNudAUAAAAAilDmoHb11Vdr5cqVhda/9957GjRokE+Kgp/s+aTgtZPQBgAAAJhFmac+tmnTRvPnz9eGDRvUrVs3SdJXX32lzZs3a+rUqXrsscfc+06aNMl3lcL30o8WvLZFGFcHAAAAAA9lDmovvPCCqlevrh07dmjHjh3u9dWqVdMLL7zgXrZYLAS1yqTTjUZXAAAAAOBPZQ5q+/fvr4g6UBmNXy85HVKQ1ehKAAAAgCqlXA+8BiRJ0XFGVwAAAABUSeV64DUAAAAAoOIQ1AAAAADAZJj6iPLb+pKUe1oKiZS6jDW6GgAAAKDKIKih/DYuktKTpOgEghoAAADgQ+Wa+vjFF1/o73//u7p166bDhw9Lkl599VVt2rTJp8UBAAAAQCAqc1B7++231b9/f4WHh+v7779XTk6OJCk1NVULFizweYEAAAAAEGjKHNTmzZunp59+Ws8995xsNpt7/WWXXabvvvvOp8WhAmx+THp5kPTSX6WvnzG6GgAAAABFKPM1art27dIVV1xRaH1sbKxOnTrli5pQUZL3SmtmFb0tONS/tQAAAAAoVplH1OLi4rR3795C6zdt2qQmTZr4pChUkMzkotfHd5Ca9PJvLQAAAACKVeYRtVtuuUW33367XnzxRVksFiUlJWnLli2aNm2aZs0qZrQG5tP1n1LfufmvrSGSxWJsPQAAAADcyhzUZsyYIafTqd69eyszM1NXXHGFQkNDNW3aNN12220VUSMqQpCV6Y4AAACASZU5qFksFt19992aPn269u7dq4yMDLVp00ZRUVEVUR8AAAAABJxyP/A6JCREbdq08WUtqGxqNpXCYqTI2kZXAgAAAFQpZQ5qvXr1kqWE65k+++yz8yoIlciYD42uAAAAAKiSyhzUOnbs6LFst9v1ww8/6Oeff9bo0aN9VRcAAAAABKwyB7VHHnmkyPVz5sxRRkbGeRcEAAAAAIGuzM9RK87f//53vfjii746HQAAAAAErHLfTORcW7ZsUVhYmK9Oh8rg7XFSZooUUVMa+rzR1QAAAABVRpmD2nXXXeex7HK5dOTIEW3dupUHXhvNkSe9f5t08Muit9uzfdvegc1SepIUneDb8wIAAAABrsxBLTY21mM5KChILVu21H333ad+/fr5rDCUw4HPpR+Xe7dvCM+9AwAAAMyqTEHN4XBo7NixateunapXr15RNaG8ctI9lyNqFb1fzaZSx5EVXw8AAACAcilTULNarerXr5927txJUDO7vvdLl00yugoAAAAA5VDmuz5eeOGF2rdvX0XUAgAAAABQOYLavHnzNG3aNH344Yc6cuSI0tLSPL4AAAAAAOfH66mP9913n6ZOnaqBAwdKkq6++mpZLBb3dpfLJYvFIofD4fsqYT4bHpAyjhpdBQAAAFAleR3U5s6dq//7v//T+vXrK7IeVBbZaZLLmf86lDtIAgAAAL7kdVBzuVySpJ49e1ZYMahEwmLyn58WGiX1utvoagAAAIAqpUx3fTx7qiMCzMOtCx5uPXWndOWM/C8AAAAAPlemoNaiRYtSw9qJEyfOqyAAAAAACHRlCmpz585VbGxsRdUCAAAAAFAZg9qIESNUp06diqoF5bXzQ+mjKdLpZKMrAQAAAOADXgc1rk8zsa+ekjKOea4Lr25MLQAAAADOW5nv+ggTyssueB3fQap7odT2WsPKAQAAAHB+vA5qTqezIuuAr0z43OgKAAAAAJynIKMLAAAAAAB4IqgBAAAAgMkQ1AAAAADAZMp0e34EsOuelRw5kjXU6EoAAACAKo+gBu80vtzoCgAAAICAwdRHAAAAADAZghoAAAAAmAxTHyszR56UdUJy5FZ8W/u/KLhGjWmQAAAAQIUiqFVWqYelF/pKaYf9094746X0JCk6QZq60z9tAgAAAAGKqY+V1e7VhUNadLwxtQAAAADwKUbUKiuno+B1fAepRlOpy1jj6gEAAADgMwS1qqDbRKn9MKOrAAAAAOAjTH0EAAAAAJMhqAEAAACAyRDUAAAAAMBkCGoAAAAAYDIENQAAAAAwGYIaAAAAAJgMt+eHd6buNLoCAAAAIGAQ1MzsdIp06reit6Ue9G8tAAAAAPyGoGZWv38jvfxXyZFrdCUAAAAA/Ixr1Mxqz6feh7TqjSu2FgAAAAB+xYiaWblcBa9b/lWKrVf0fhdcIl3QpeLr2fCAlJ0mhcVIV86o+PYAAACAAEZQqwwuuUVq2svYGrYtk9KTpOgEghoAAABQwZj6CAAAAAAmQ1ADAAAAAJMhqAEAAACAyRDUAAAAAMBkuJlIIPplpbR+gZST4bm+ZlNpzIee694eJx3YLGUc9V99AAAAQIAjqAWi9Quk5N2F14fFFF6XmZJ/t8czQqMqri4AAAAAkghqgenMSJolSIqKK1gfWbvwvhE182/JL+WHtF53V3x9AAAAQIAjqJlVdmrFtxEVJ03dWfI+Q5+v+DoAAAAAeOBmImbksEvfPmd0FQAAAAAMQlAzoxP7PZdrtzSmDgAAAACGYOqj2dVpK8Uk+PacPe+Uck9LIZG+PS8AAAAAnzD1iJrD4dCsWbPUuHFjhYeHq2nTprr//vvlcrnc+1gsliK/HnzwwWLPO2fOnEL7t2rVyh9vqewSOvr+nF3GSt0n5n8HAAAAYDqmHlH7z3/+o6eeekrLli1T27ZttXXrVo0dO1axsbGaNGmSJOnIkSMex3z88ce6+eabNXTo0BLP3bZtW61du9a9HBxs6q4AAAAAEEBMnU6+/PJLXXPNNfrrX/8qSWrUqJFef/11ffPNN+594uLiPI5577331KtXLzVp0qTEcwcHBxc61jxcpe8CAAAAoMoydVDr3r27nn32We3evVstWrTQjz/+qE2bNmnx4sVF7n/s2DF99NFHWrZsWann3rNnjxISEhQWFqZu3bpp4cKFatCgQbH75+TkKCcnx72clpYmSbLb7bLb7WV8Z57OHO8+T16ebH9uczpdcpzn+QtJPyq5nPnPUYs2a1itWIX6HH5BvxuDfjcG/W4M+t0Y9Lsx6Hdj+Ku/La6zL/gyGafTqbvuukuLFi2S1WqVw+HQ/PnzNXPmzCL3X7RokR544AElJSUpLCys2PN+/PHHysjIUMuWLXXkyBHNnTtXhw8f1s8//6zo6Ogij5kzZ47mzp1baP3y5csVERFRvjdYjKjsw+q9M/89/lbjcv3Q8Bafnr/fz7cr3H5SWbbq+vTCR316bgAAAKAqy8zM1KhRo5SamqqYmJgKa8fUQW3FihWaPn26HnzwQbVt21Y//PCDJk+erMWLF2v06NGF9m/VqpX69u2rxx9/vEztnDp1Sg0bNtTixYt18803F7lPUSNq9evXV3Jy8nn/gOx2u9asWaO+ffvKZrNJf+yS7dnLJEnO9qPkGPzYeZ3/XMGPtZMl/Yhc0fHKm7Tdp+euLAr1OfyCfjcG/W4M+t0Y9Lsx6Hdj0O/GSElJUXx8fIUHNVNPfZw+fbpmzJihESNGSJLatWun3377TQsXLiwU1L744gvt2rVLb7zxRpnbqVatmlq0aKG9e/cWu09oaKhCQ0MLrbfZbD77h+E+11k3NgmyBinI5//wLH/+ryXg/1H78ucH79HvxqDfjUG/G4N+Nwb9bgz63b/81demvj1/ZmamgoI8S7RarXI6nYX2feGFF9S5c2d16NChzO1kZGQoMTFR8fHx5a4VAAAAAHzF1EFt8ODBmj9/vj766CMdOHBAK1eu1OLFizVkyBCP/dLS0vTWW29p3LhxRZ6nd+/eWrp0qXt52rRp2rhxow4cOKAvv/xSQ4YMkdVq1ciRIyv0/QAAAACAN0w99fHxxx/XrFmz9K9//UvHjx9XQkKCJkyYoNmzZ3vst2LFCrlcrmKDVmJiopKTk93Lhw4d0siRI5WSkqLatWurR48e+uqrr1S7du0KfT/eM+1lgwAAAAD8wNRBLTo6WkuWLNGSJUtK3G/8+PEaP358sdsPHDjgsbxixQofVOcvFqMLAAAAAOBnpp76CAAAAACBiKAGAAAAACZDUDMj8z7aDgAAAIAfmPoaNUiyVMA1aqPfl5x5UhA/fgAAAMCM+KQeiGo1N7oCAAAAACVg6iMAAAAAmAxBzZS4Rg0AAAAIZEx9NL0KuEbtp7cke6Zki5Da/8335wcAAABwXghqgWjNbCk9SYpOIKgBAAAAJsTUR7M5eUDa8IDRVQAAAAAwECNqZvPOBOn3rwqWK+L2/AAAAABMjRE1szmReNaCRWrW17BSAAAAABiDETUzm7xdqlbf6CoAAAAA+BkjamZVrQEhDQAAAAhQBDUAAAAAMBmCGgAAAACYDEENAAAAAEyGm4kEoqg6nt8BAAAAmApBLRBN2Gh0BQAAAABKwNRHAAAAADAZghoAAAAAmAxTH83EmSed/qP47b+slNYvkHIy8pdDIqXbtnru8+k90va3S24nooY0YrlUveH51QsAAACgQhDUTMSS+FnBgsNeeIf1C6Tk3QXLIdGF98k6JaUnldxQWIy0Zlb+Q7X7zStXrQAAAAAqDkHNRCxnByxbROEdzoykWYKkqLj8EbVzhVeTohNKbig7TTq+U2p7XblrBQAAAFBxCGpmdfnU4rdFxUlTdxa9rd88RskAAACASo6biQAAAACAyRDUAAAAAMBkCGoAAAAAYDIENQAAAAAwGW4mUpkMekTKy5KCw42uBAAAAEAFIqhVJi0HGF0BAAAAAD9g6iMAAAAAmAxBDQAAAABMhqmPZpJ+tOTtSd9LDrtktUkJF/mnJgAAAAB+R1AzEcsfOwsWHDmFd3h9lJSeJEUnSFN3Ft4OAAAAoEpg6qOJuCJrFyzUamFcIQAAAAAMRVAzq9AYoysAAAAAYBCCGgAAAACYDEENAAAAAEyGoAYAAAAAJkNQAwAAAACTIagBAAAAgMkQ1AAAAADAZAhqAAAAAGAywUYXgDKY+I3kckkWi9GVAAAAAKhABDUTsX73csk7hEb7pQ4AAAAAxmLqo0lYXA7PFaFRxhQCAAAAwHAENZOwuJyeK2o0MaYQAAAAAIZj6qNpuApeNuxR9C5fLpVy0vOnQHaf6J+yAAAAAPgdQc2MirtZyJYnpPQkKTqBoAYAAABUYUx9BAAAAACTIagBAAAAgMkQ1EzCcvY1agAAAAACGkENAAAAAEyGoGZGxd1MBAAAAEBAIKiZBTMfAQAAAPyJoAYAAAAAJkNQM42zh9SY+ggAAAAEMh54XZnEd5Bi60kRtYyuBAAAAEAFIqiZhMft+Yu7mcioFf4pBgAAAIChmPoIAAAAACbDiJopnTOitnyElJmcP+WRUTUAAACgyiOoVQZHfpTSk6ToBKMrAQAAAOAHTH00DR6kBgAAACAfQc2MiruZCAAAAICAQFAzCxcjagAAAADyEdRMiRE1AAAAIJAR1AAAAADAZAhqJuExhsY1agAAAEBAI6iZhEVOo0sAAAAAYBIENZOIzDlesHBin3GFAAAAADAcD7w2ozptPJe73SrlpEuh0cbUAwAAAMCvCGqmcdbt+WMv8NzUfaJ/SwEAAABgKKY+mhI3EwEAAAACGUENAAAAAEyGqY+mcdbUx3Nvz5+TLrlc+eu5Tg0AAACo8ghqlcHSS6T0JCk6QZq60+hqAAAAAFQwpj6ahevsBa5RAwAAAAIZQQ0AAAAATIagZhIWzyE1AAAAAAGMoGZG595MBAAAAEBAIagBAAAAgMkQ1EyDqY8AAAAA8pk6qDkcDs2aNUuNGzdWeHi4mjZtqvvvv18uV0GoGTNmjCwWi8fXgAEDSj33E088oUaNGiksLExdu3bVN998U5FvBQAAAAC8ZurnqP3nP//RU089pWXLlqlt27baunWrxo4dq9jYWE2aNMm934ABA/TSSy+5l0NDQ0s87xtvvKEpU6bo6aefVteuXbVkyRL1799fu3btUp06dSrs/XiNa9QAAACAgGbqEbUvv/xS11xzjf7617+qUaNGuv7669WvX79Co1+hoaGKi4tzf1WvXr3E8y5evFi33HKLxo4dqzZt2ujpp59WRESEXnzxxYp8OwAAAADgFVOPqHXv3l3PPvusdu/erRYtWujHH3/Upk2btHjxYo/9NmzYoDp16qh69eq66qqrNG/ePNWsWbPIc+bm5mrbtm2aOXOme11QUJD69OmjLVu2FFtLTk6OcnJy3MtpaWmSJLvdLrvdfj5vU3a73eP2/A6nS86zz/m3V2Rx2OWy2qTzbAv5zvzMzvdnh7Kh341BvxuDfjcG/W4M+t0Y9Lsx/NXfpg5qM2bMUFpamlq1aiWr1SqHw6H58+frhhtucO8zYMAAXXfddWrcuLESExN111136S9/+Yu2bNkiq9Va6JzJyclyOByqW7eux/q6devq119/LbaWhQsXau7cuYXWf/rpp4qIiDiPd5nv7Fi5b98+7chZVfSO3yedd1sosGbNGqNLCEj0uzHod2PQ78ag341BvxuDfvevzMxMv7Rj6qD25ptv6rXXXtPy5cvVtm1b/fDDD5o8ebISEhI0evRoSdKIESPc+7dr107t27dX06ZNtWHDBvXu3dtntcycOVNTpkxxL6elpal+/frq16+fYmJizuvcdrtd360sCIlNmjRRo94Dz+ucKJndbteaNWvUt29f2Ww2o8sJGPS7Meh3Y9DvxqDfjUG/G4N+N0ZKSopf2jF1UJs+fbpmzJjhDmPt2rXTb7/9poULF7qD2rmaNGmiWrVqae/evUUGtVq1aslqterYsWMe648dO6a4uLhiawkNDS3yJiU2m803/zDOuju/1WqVlX9sfuGznx/KhH43Bv1uDPrdGPS7Meh3Y9Dv/uWvvjb1zUQyMzMVFORZotVqldPpLPaYQ4cOKSUlRfHx8UVuDwkJUefOnbVu3Tr3OqfTqXXr1qlbt26+KdzXdq2WflmZ/x0AAABAlWfqEbXBgwdr/vz5atCggdq2bavvv/9eixcv1k033SRJysjI0Ny5czV06FDFxcUpMTFRd955p5o1a6b+/fu7z9O7d28NGTJEEydOlCRNmTJFo0ePVpcuXXTJJZdoyZIlOn36tMaOHWvI+8x39gOvz7k9/4d3SOlJUnSC1LL0Z8QBAAAAqNxMHdQef/xxzZo1S//61790/PhxJSQkaMKECZo9e7ak/NG1n376ScuWLdOpU6eUkJCgfv366f777/eYppiYmKjk5GT38vDhw/XHH39o9uzZOnr0qDp27KjVq1cXusEIAAAAgMCUmZunL/YkKzfPczZfeuopv7Rv6qAWHR2tJUuWaMmSJUVuDw8P1yeffFLqeQ4cOFBo3cSJE90jbKbDA68BAAAAQ4189iv9eCi10Hpnjn/u+mjqa9QCicVj6iMAAAAAo9gdziJDmj+ZekQtcDGiBgAAAJhB/RrhGtO9sXs5I+2UJi+p+HYJagAAAABQjPiYcN3coyCopaSkaLIf2mXqo9k93kXKOGp0FQAAAAD8iKBmGmddo3b2zUTSj0quP+80Exrl35IAAAAAGIKpj2YXHSflRueHtF53G10NAAAAAD8gqJnSWSNqt201rgwAAAAAhmDqo0lYXNyeHwAAAEA+RtTMyGKRPr1HyjolhVeT+s0zuiIAAAAAfsSImlltf1v6/tX87wAAAAACCiNqpnH21EceeA0AAIDKJc/h1Nqdx3TwRKbRpZw3h9PoCghqplH9dOJZS1yvBgAAgMplxbe/6553fza6jCqDqY8m4QyyFSzkVv6/QgAAACCw/Ho0zegSKsTFjasb0i4jambU4FLpl5VGVwEAAACUy8y/tFKDGhFGl3HeakSG6OJGNQxpm6AGAAAAwKcua1ZLF9aLNbqMSo2pjwAAAABgMgQ1AAAAADAZghoAAAAAmAzXqJlVi35S1kkp3Ji7zAAAAAAwDkHNrAY/anQFAAAAAAzC1EcAAAAAMBmCGgAAAACYDFMfTcLmyDS6BAAAAASwbLtDb3z7u/Ynny7X8d/uP+njigIbQc0k6qRtL1hwOaVnekoZx6WoOtKEjcYVBgAAgIDw9neHdO/7vxhdBv7E1EeTOB1ap2ChRpP8kJaelP8dAAAAqGAHU3wzw6thzQi1jIv2ybkCGSNqZhRRw+gKAAAAEMDmD7lQbRNiy3xckEVqFRcjm5XxoPNFUAMAAADgoXmdaHWsX83oMgIaURcAAAAATIagBgAAAAAmQ1ADAAAAAJMhqAEAAACAyRDUAAAAAMBkCGoAAAAAYDLcnt+s+t4n2TMlW4TRlQAAAADwM4KaSdQ4vcdzRfu/GVMIAAAAqpwjqVl6akOijqVlF7vPrqPpfqwIpSGomUS4/WTBQhA/FgAAAPjOk+sT9epXv3m9vzXIUoHVwBtco2YSeZaQgoXoOOMKAQAAQJVT0kjauS6sF6P2F8RWYDXwBkM3JmGRK/9F3QvzvyfvkZx5+aNrtZobVxgAAACqlFWTLletqJCiN1qk2lGhslgYUTMaQc10/vxHsexqKT1Jik6Qpu40tiQAAABUGbWiQ1QnOszoMlAKpj4CAAAAgMkQ1Ezjz6mPjDIDAAAAAY+gZhLua9RIagAAAEDA4xo1s3DnNIv0y8r869MAAAAAVKiPfjqixWt26XSOo9h91k7tqahQ/0YngpppnDWitn5BwerQKEOqAQAAAALB4jW7lPjH6RL3cblcJW6vCAQ1k3BPfbRYpJyMgg297jamIAAAACAAnBlJC7Ko2LthGvG4AoKa6Zz1SxCdILW91rBKAAAAgEBRJzpMX93V2+gy3AhqZsPDBQEAAHCeVnxzUG9s/V0OZ/6srf3JJU/tC2TP3dhFuQ6nQqzmus8iQc0kCu76KGn8esnpkIKsxhUEAACASikzN0+z3/9FuXnOIreHWvmMebZ2F8QaXUKRCGqmY5Gi44wuAgAAAJVUVq7DI6RZgyzu76MuaaDYCJtRpaEMCGpmcPZdZJj6CAAAAB/p07qOnh99sdFloBwIaqZw9u0+CWoAAACAv6zbeUzZdqfCbEHq3bqu0eW4EdTM4NwRta0vSbmnpZBIqctY4+oCAAAAqri7V/6so2nZiosJI6jhXOeMqG1cJKUn5d+en6AGAAAABBxz3YMyUHGNGgAAAICzENRMgWvUAAAAABQgqJkNI2oAAABAwCOomcHZUx8BAAAABDyCmikw9REAAABAAe76aAbcTAQAAADneHTtHr3+zUHlOYubfeVSTo5V92/foLP/2O9itlaVQFAzg7RDBa8zU4yrAwAAAKaQlm3Xo+t2q9iM5mZRuj232K1RoXzcr6z4yZlBXk7BawuzUQEAAAJdjt3pDmlhtiDVigotvJPLpcysLEWEhxc5K6tOdKhu7tGkgiut/CJCrYoKDVZEqNXoUjwQ1MzmgoullL1SWIwUWdvoagAAAGCwy5vX1nM3dim03m63a9WqVRo48ArZbDYDKqsaPpt6pdElFImgZkZjPjS6AgAAAAAGYp4dAAAAAJgMQc0MuDMPAAAAgLMw9dFsuD0/AAAA4DcLVu1UaqZdsRE23TWwtdHluBHUzOjtcfm36Y+oKQ193uhqAAAAgCrr/R+SdDQtW3ExYQQ1lOLAZik9SYpOMLoSAAAAAAbgGjVT4Bo1AAAAAAUIaqbDNWoAAABAoCOoAQAAACYz54NfjC4BBiOoAQAAACaSmZunj3464l6uGRliYDUwCkENAAAAMJE8p+f9C/6vZ1ODKoGRCGpmwAOvAQAAUIQrWtRWo1qRRpcBAxDUzIYHXgMAAAABj+eoAQAAAAhYvVrVUWpWrmLDzXUtIEHNjDqPlrLTpLAYoysBAAAAqrSF17UzuoQiEdTM6MoZRlcAAAAAwEBco2YK3EwEAAAAQAGCmulwMxEAAAAg0DH10Wy2vyUd3CKNWC5Vb2h0NQAAAIZYsGqn3v8hqdT9erWqU+gao8GPb9If6TmlHjtzYCtd07Geeznxjwzd8NzXXtX3/sTLVCcmzL28/OuDemzdnlKPa1wrUq+Pv9Rj3e0rvtfX+064l51nPbrpq30punTBOknSiEvqa3KfFh7Hzt5m1YJfNspSyh/7HxneUd2a1nQvb0lM0R1v/FBqvZL01V29PZaXrN2tFd/8XupxXZvU0KMjLvJYN/LZr7Q/+XSpx07q3VyjujZwLx9Py9bVSzd7Ve9rt3RV09pR7uX3fjishat+LXb/o2nZkqR29WL1wW09vGrDHwhqZpN9Kv/r0fZSrRbSxG+NrggAAMDvUjPt7g/QJe6XlVto3R/pOV4dm5Xr8Fh2OF1eHSdJjnOeg5uZm+fVsdFhhT9+nzidW+yxuXlO97b07LxC21NzLUrNLT2U5jqchZa9fa/nSs/27r2eOF34Z5Oc4d3PJjPX8706XGX42ZzzwPCsXEe52jQaQc0ELGnn/LXIEiTVbCb1utuYggAAAPzoqoc36HhajurEhOqzqVdKkmIjbIo7a8SqOEXdUr12dKhX7YaHWD2WrUEWr9qUJOs5z76NCAn26thaUYVrqxEZ4j425XSO7I6CoBESHKQaEfnvsaiQFxviUlhYWKkjaiHWoELL3r7Xc0WHefdea0QW/tnUigotMnCeKyLE871aLWX42QR59kV4iLXUYyNDrZrar6VX5/cXUwc1h8OhOXPm6L///a+OHj2qhIQEjRkzRvfcc48sFovsdrvuuecerVq1Svv27VNsbKz69OmjBx54QAkJCcWed86cOZo7d67HupYtW+rXX4sfEq1IlrTDniui4hhJAwAAASMzx6GMnDxF5RR8NL1rYGvdNbB1uc5X3ulrTWtHFZrm561RXRt4TNUrizPTAxP/yFDvhzd6bLvuonp6YGj7Yo+9r7NDAwf2lM1mK1Ob3ZrWLPd7ndynRaEpmN46d9qnt+rEhJW73ms61vOY4lpZmDqo/ec//9FTTz2lZcuWqW3bttq6davGjh2r2NhYTZo0SZmZmfruu+80a9YsdejQQSdPntTtt9+uq6++Wlu3bi3x3G3bttXatWvdy8HBBnaFxVr6PgAAAKjSnGdN2WtQI0L92tTVuMubGFgRjGTqoPbll1/qmmuu0V//+ldJUqNGjfT666/rm2++kSTFxsZqzZo1HscsXbpUl1xyiQ4ePKgGDYr/q0ZwcLDi4uIqrngAAABUKIfDIbvdbnQZPuOw56pedP4f8Pu2rK5pffJDWnZ20ddX2e12BQcHKzs7Ww6Ho8h9UHY2m01Wq/EDKaYOat27d9ezzz6r3bt3q0WLFvrxxx+1adMmLV68uNhjUlNTZbFYVK1atRLPvWfPHiUkJCgsLEzdunXTwoULSwx2OTk5yskpuFAzLS1NUv4/kPP9D4TLkaezfxVccimvCv1Hx4zO/Myq0n/cKwP63Rj0uzHod2PQ78Y43353/flMWZdcXp3D5XLp+PHj7s9jVUWew6U5vepIkiJDLNq3b1+J+7tcLsXFxengwYOyWHjEky/FxMSoTp06Rfarv/77YnG5XKZ92rLT6dRdd92lRYsWyWq1yuFwaP78+Zo5c2aR+2dnZ+uyyy5Tq1at9NprrxV73o8//lgZGRlq2bKljhw5orlz5+rw4cP6+eefFR0dXeQxRV3XJknLly9XRERE+d7gnxr/sUbtD73qXs6yVdenFz56XucEAACoLGZvsyo116LYEJfu61z6yFB0dLSqV6+uWrVqKSQkpMqElDyH9Mef4wLhVqmad/dEgQ+5XC7l5uYqOTlZJ0+eVHp6eqF9MjMzNWrUKKWmpiomJqbCajF1UFuxYoWmT5+uBx98UG3bttUPP/ygyZMna/HixRo9erTHvna7XUOHDtWhQ4e0YcOGMnXaqVOn1LBhQy1evFg333xzkfsUNaJWv359JScnn/cPyPX1MwpZW3CHR1d0vPImbT+vc6Jkdrtda9asUd++fct88S3Kj343Bv1uDPrdGPS7Mc6333s8uFHH0nJUNyZUm6b3LHFfh8Ohffv2qXbt2qpZs2aJ+1Y2OXlO7TmeIUmqHhGietVKvlOhy+VSenq6oqOjq0xYNYuUlBT98ccfatKkSaFpkCkpKYqPj6/woGbqqY/Tp0/XjBkzNGLECElSu3bt9Ntvv2nhwoUeQc1ut2vYsGH67bff9Nlnn5W5w6pVq6YWLVpo7969xe4TGhqq0NDCf9aw2Wzn/X8EjqCzbpd62e2yNOP/XPzFFz8/lB39bgz63Rj0uzHod2OUt9/P3FreIkupxzscDlksFkVFRSkoKKjEfSsbi6Vg/MQilfr+nE7nn8dZqlxfGC0qKkrJycmSVOh30l//bTH1TzQzM7PQL53VanX/UkoFIW3Pnj1au3Ztuf6ykpGRocTERMXHx593zeVz1qBm3XZS48sNqgMAAKByYAQJFckMv1+mDmqDBw/W/Pnz9dFHH+nAgQNauXKlFi9erCFDhkjKD2nXX3+9tm7dqtdee00Oh0NHjx7V0aNHlZtb8CT03r17a+nSpe7ladOmaePGjTpw4IC+/PJLDRkyRFarVSNHjvT7eyzEBL8UAAAAAIxl6qmPjz/+uGbNmqV//etfOn78uBISEjRhwgTNnj1bknT48GG9//77kqSOHTt6HLt+/XpdeeWVkqTExET30KUkHTp0SCNHjlRKSopq166tHj166KuvvlLt2rX98r4KMe9lggAAABVu/pALlW13Ksxm6jEEU2rfvr3uuOMO3XHHHUaXct4sFotWrlypa6+9VgcOHFDjxo31/fffF/qcHyhMHdSio6O1ZMkSLVmypMjtjRo1kjf3Qjlw4IDH8ooVK3xQnS+d9R6O/ixF1WX6IwAACBi9W9c1ugSfy7E7ZHcUfMaLDi/5uqaZd8/StJn3lLmdzz777LyfDXzllVdq48aN7uU6deroiiuu0EMPPaSGDRue17nLq379+jpy5Ihq1aplSPtmYOqgFpA2PyL99IY0dafRlQAAAKAcTpzO1aGTmR7r1m371f36kw9W6smHF+i9Dd+610VERrqPcblcysvL86qtWrVqnfejoiTplltu0X333SeXy6XffvtNkydP1t///nd98cUX533u8rBarecdQCs7xpfNgKmPAAAAVUZGduEHIteqU9f9FRUdI4vF4l7en7hH3VrV16b1azRi4JXq0rSuvt/6lRITE3XNNdeobt26ioqK0sUXX6y1a9d6nLd9+/Z69NGC5+9aLBY9//zzGjJkiCIiItS8eXP3pUIliYiIUFxcnOLj43XppZdq4sSJ+u6779zbHQ6Hbr75ZjVu3Fjh4eFq2bKlR7uStGHDBl1yySWKjIxUtWrVdNlll+m3335zb3/vvffUqVMnhYWFqUmTJpo7d26xgfTAgQOyWCz64Ycf3Oe2WCxat26dunTpooiICHXv3l27du3yOK4sbZgdQc0UCGoAACBwbT+Uqm2/ndT2Q6lGl+JzNSNDVDs61OMrOixYFlncy9X+nBb5xH/u1+z75mnL1h90+SWdlZGRoYEDB2rdunX6/vvvNWDAAA0ePFgHDx4ssc25c+dq2LBh+umnnzRw4EDdcMMNOnHihNc1nzhxQm+++aa6du3qXud0OnXBBRforbfe0o4dOzR79mzdddddevPNNyVJeXl5uvbaa9WzZ0/99NNP2rJli8aPH+++e+IXX3yhG2+8Ubfffrt27NihZ555Ri+//LLmz59fpv68++679fDDD2vr1q0KDg7WTTfd5N7mqzbMgqmPAAAAMNQtr2zV0bRsxcWE6au7epfrHIMf36Q/0nN8XFnJakeH6oPbepS6T0iw5wOTq0WEyGKR4mPDJUk1o/Kf1btg/v265ppB7v1q1aqpDh06uJfvv/9+rVy5Uu+//74mTpxYbJtjxoxx3818wYIFeuyxx/TNN99owIABxR7z5JNP6vnnn5fL5VJmZqZatGihTz75xL3dZrNp7ty57uXGjRtry5YtevPNNzVs2DClpaUpNTVVgwYNUtOmTSVJrVu3du8/d+5czZgxw/0s5CZNmuj+++/XnXfeqXvvvbfYus41f/589eyZ/1D0GTNm6K9//auys7MVFhbmszbMgqBmBkx9BAAAOC9/pOfoaFq20WWcly5dungsZ2RkaM6cOfroo4905MgR5eXlKSsrq9QRtfbt27tfR0ZGKiYmRsePHy/xmBtuuEF33323JOnYsWNasGCB+vXrp23btik6OlqS9MQTT+jFF1/UwYMHlZWVpdzcXPcdGWvUqKExY8aof//+6tu3r/r06aNhw4a5n1P8448/avPmzR6jWw6HQ9nZ2crMzPT6Oruz39uZcx8/flwNGjTwWRtmQVAzBYIaAADA+agdHVrp24yMjPRYnjZtmtasWaOHHnpIzZo1U3h4uK6//nqP5wUXxWbzvMOkxWKR0+ks8ZjY2Fg1a9ZMktSsWTO98MILio+P1xtvvKFx48ZpxYoVmjZtmh5++GF169ZN0dHRevDBB/X111+7z/HSSy9p0qRJWr16td544w3dc889WrNmjS699FJlZGRo7ty5uu666wq1HRYWVmJtxb23M9Mqz7w3X7VhFgQ1AAAAVHqlTUGsjDZv3qwxY8ZoyJAhkvKDyLmPnaooVmv+dM2srCx3Ld27d9e//vUv9z6JiYmFjrvooot00UUXaebMmerWrZuWL1+uSy+9VJ06ddKuXbvcYbAi+KMNfyKomQFTHwEAAHCO5s2b65133tHgwYNlsVg0a9asUkfGyiszM1NHjx6VlD/18f7771dYWJj69evnruWVV17RJ598osaNG+vVV1/Vt99+q8aNG0uS9u/fr2effVZXX321EhIStGvXLu3Zs0c33nijJGn27NkaNGiQGjRooOuvv15BQUH68ccf9fPPP2vevHk+eQ/+aMOfuOujKRDUAAAA4Gnx4sWqXr26unfvrsGDB6t///7q1KlThbT13HPPKT4+XvHx8erVq5eSk5O1atUqtWzZUpI0YcIEXXfddRo+fLi6du2qlJQUj9G1iIgI/frrrxo6dKhatGih8ePH69Zbb9WECRMkSf3799eHH36oTz/9VBdffLEuvfRSPfLIIz59oLY/2vAnRtQAAAAAPxozZozGjBnjXr7yyivlKmKGVaNGjfTZZ595rLv11ls9ln/66SfFxMS4l4s6z6lTp0qsZ8OGDaXWHBoaqpdeekkvvfSSx/qFCxdKkurWrauVK1eWeI7+/furf//+xW4/u/ZGjRp5LBfVRx07diy0rrQ2KhOCmhmc/fs1/L9S68GGlQIAAADAeEx9NAPn2U+vtxhWBgAAAM6fk6ta4AMENRMI+vqpggULQQ0AAKAyS8u2l74TUAqmPpqAq0ZTWZK25S/UvdDYYgAAAPxs7dSecrlc7udiVXY2a5Dsjvy7MwZbGRdB+RDUzObrZ6SwGOnKGUZXAgAA4BdRoVX3I2lQFQmf8D8ivimcNZH5qyekbcuMKwUAAAA+YWM0DeeB3x4T4bpTAAAAABJTH82hiOddAAAABIrnv9in9Ow8RYcFa9zlTYwuBzAFghoAAAAM9fwX+3U0LVtxMWEENeBPTH00BUbUAAAAUPVt2LBBFotFp06dkiS9/PLLqlatmqE1mRVBDQAAAKhgFoulxK85c+aU+9xWq1XvvvtumWoIDg5WgwYNNGXKFOXk5JS77fM1fPhw7d6927D2zYypj2bANWoAAABV2pEjR9yv33jjDc2ePVu7du1yr4uKivJLHS+99JIGDBggu92uH3/8UWPHjlVkZKTuv/9+v7R/rvDwcIWHhxvSttkxogYAAABUsLi4OPdXbGysLBaLx7oVK1aodevWCgsLU6tWrfTkk0+6j83NzdXEiRMVHx+vsLAwNWzYUAsXLpQktW/fXpI0ZMgQWSwWNWrUqMQ6qlWrpri4ONWvX1+DBg3SNddco++++869PTExUddcc43q1q2rqKgoXXzxxVq7dq3HOZ588kk1b95cYWFhqlu3rq6//nr3NqfTqYULF6px48YKDw9Xhw4d9L///a/Yes6d+jhnzhx17NhRr776qho1aqTY2FiNGDFC6enp5W6jsmJEDQAAAFXC81/s0/Nf7C91vwvrxej50Rd7rBu37Fv9fDit1GPHXNZIQztdIFcJM6KczrLNlnrttdc0e/ZsLV26VBdddJG+//573XLLLYqMjNTo0aP12GOP6f3339ebb76pBg0a6Pfff9fvv/8uSfrss8/UvHlz90iZ1Wr1ut3du3frs88+05gxY9zrMjIyNHDgQM2fP1+hoaF65ZVXNHjwYO3atUsNGjTQ1q1bNWnSJL366qvq3r27Tpw4oS+++MJ9/MKFC/Xf//5XTz/9tJo3b67PP/9cf//731W7dm317NnTq7oSExP17rvv6sMPP9TJkyc1bNgwPfDAA5o/f77P2qgMCGomYHHfTMQiNe0lRdQ0tB4AAMzmo5+OaPGaXTqd4yhxP5dcuiAkSAPPWT/y2a+0P/l0qe1M6t1co7o2cC8fT8vW1Us3e1Xja7d0VdPaBdPX3vvhsBau+rXU42pHh+qD23p4rJv5znat//V4qcde3TFBdw1s7bHuqoc3KLOUfpKk+UMuVO/Wdd3L2w+l6pZXtpZ6nCStndpTUaEFHyNf3HxAT26zasEvG2WRpdjjigtIx9OzvWq3NOnZeTqaVvq54quFFVqXcjrXq2MPn8zUkdSsctVXnHvvvVcPP/ywrrvuOklS48aNtWPHDj3zzDMaPXq0Dh48qObNm6tHjx6yWCxq2LChpPyRpVq1akkqGCkrzciRI2W1WpWXl6ecnBwNGjRIM2fOdG/v0KGDOnTo4F6+//77tXLlSr3//vuaOHGiDh48qMjISA0aNEjR0dFq2LChLrroIklSTk6OFixYoLVr16pbt26SpCZNmmjTpk165plnvA5RTqdTL7/8sqKjoyVJ//jHP7Ru3TrNnz/fZ21UBgQ1MwkKlv6x0ugqAAAwnSOpWbqyZR3tOpquTXuTS9y3emzhdckZOV59CM/MzfNYdrhcXh0nSY5zRlGych1eH3uu1CzvQkNqpr3QuuNpOcrIyStib0/ZdqfHcq7D6XW9544mZeTkKTXXotTckm9KUVxAOtN1kaHejwYVJTosWHExhds4V83IkCLXeXNsSLD3NUaGlv5R+/Tp00pMTNTNN9+sW265xb0+Ly9PsbH5v8xjxoxR37591bJlSw0YMECDBg1Sv379vK7jbI888oj69Okjh8OhvXv3asqUKfrHP/6hFStWSMofUZszZ44++ugjHTlyRHl5ecrKytLBgwclSX379lXDhg3VpEkTDRgwQAMGDNCQIUMUERGhvXv3KjMzU3379vVoMzc31x3mvNGoUSN3SJOk+Ph4HT+e/4cLX7VRGRDUzICbiQAAUKJxlzfR7ycydcsrW0v8MO2SS5G2wqMdtaJClZ5deniJCPH8aGS1WLz68C5J1iDPkaTwEKtXx9aODi20Ljbcu9AQG2ErtK5OTKiickr/iBdm87xVQYg1yOv3arF4vteo0GDFhrgUFhZW4ohaSQEpMtSqqf1aetV+ccZd3qTcz2E7d6SvOD8fTpXzz89ujWpGFrtfkMXiVfDMyMiQJD333HPq2rWrx7Yz0xg7deqk/fv36+OPP9batWs1bNgw9enTR2+++aZXNZ8tLi5OzZo1kyS1bNlS6enpGjlypObNm6dmzZpp2rRpWrNmjR566CE1a9ZM4eHhuv7665WbmytJio6O1nfffacNGzbo008/1ezZszVnzhx9++237vfy0UcfqV69eh7thoYW/j0vjs3m+XttsVjkdOb/YcFXbVQGBDUzsRT/HzYAAAJd/RoRWj35ihL3sdvtWrVqVaH1r4+/tFxt1okJ01d39S7Xsdd0rKdrOtYrfcciLLyuXbmOk6TPpl5ZruPaXRBb7vd602WNFJe6QwMH9iz0Ibs03gYkswmzWRUTXrb3WpS6desqISFB+/bt0w033FDsfjExMRo+fLiGDx+u66+/XgMGDNCJEycUHBwsm80mh6P06a5FORMGs7Ly/8CxefNmjRkzRkOGDJGUH4wOHDjgcUxwcLD69OmjPn366N5771W1atX02WefqW/fvgoNDdXBgwcrbApimzZtKrwNsyCoAQAAAAaaO3euJk2apNjYWA0YMEA5OTnaunWrTp48qSlTpmjx4sWKj4/XRRddpKCgIL311luKi4tTtWrVlJGRoUaNGmndunW67LLLFBoaqurVqxfb1qlTp3T06FE5nU7t2bNH9913n1q0aKHWrfOvdWzevLneeecdDR48WBaLRbNmzXKPZknShx9+qH379umKK65Q9erVtWrVKjmdTrVs2VLR0dGaNm2a7rjjDjmdTvXo0UOpqanavHmzYmJiNHr06PPuK3+0YRYENTNx2KUnukqRtaUxHxpdDQAAAPxg3LhxioiI0IMPPqjp06crMjJS7dq10+TJkyXlh5NFixZpz549slqtuvjii7Vq1SoFBeVPX33wwQc1bdo0Pffcc6pXr16hEbCzjR07VpLcjwe44oortGDBAgUH58eCxYsX66abblL37t1Vq1Yt/fvf/1ZaWsHdMKtVq6Z33nlHc+bMUXZ2tpo3b67XX39dbdu2lZR/85HatWtr4cKF2rdvn6pVq6ZOnTrprrvu8ll/+aMNM7C4Srq3KIqVlpam2NhYpaamKiYm5rzO5XryMlmO/yyXlD+rOzpBmrrTF2WiGGemxgwcOLDMUzRQfvS7Meh3Y9DvvnXpgnU6mpatuFKmItLvxvBnv2dnZ2v//v1q3LixwsK8u6bOl85coxZms6pF3ejSD6hATqdTaWlpiomJcYc2+EZJv2cpKSmqVauWT3JASfiJAgAAAIDJENRMgUFNAAAAAAUIagAAAABgMgQ1AAAAADAZ7vpoCkx9BAAAVcP6Xce1bucxVdTt6qKCXboywaVjqVkKznKWfoCPcR8++AtBzVQsIrQBAIDKKiUjR7cs26o8Z8V9nqkXbVWXmnV0Kssui91SYe2UxriWESiY+mgG/GUGAABUAUfTcio0pJlJbDiPf0DFYkQNAAAAPte5YXXdd01bn5/XmZcrV9pxNagRoVADnqMmSdYgi0KCrYa0jcBBUDOFP//yZLVJve+VQiKNLQcAAOA8tYmPUduEWJ+fNzs7W/tPJyssJFhhIXyURdXF1EczCbJJ3SdKXcYaXQkAAKbyyPCOWnbTJXpkeEejSwFMpX379nr00UeNLqOQl19+WdWqVSvTMWPGjNG1115bIfUUpVGjRlqyZInf2isrgpoZnLlGjatSAQAoUremNdWzRW11a1rT6FKAcrFYLCV+zZkzp1zn/eyzz3TLLbeUu64ZM2aoVatWHut+/fVXWSwWjRkzxmP9yy+/rNDQUGVlZZV63uHDh2v37t3lrqs4Zg9XvsR4MQAAAFDBjhw54n79xhtvaPbs2dq1a5d7XVRUlPu1y+WSw+FQcHDpH9Vr1aqliIiIctfVq1cv/ec//9HRo0cVFxcnSVq/fr3q16+vDRs2eOy7fv16XXrppQoPDy/1vOHh4V7th+IxomYKf46ouVxS6mEp/aix5QAAAMCn4uLi3F+xsbGyWCzu5V9//VXR0dH6+OOP1blzZ4WGhmrTpk1KTEzUNddco7p16yoqKkoXX3yx1q5d63Hec6c+WiwWPf/88xoyZIgiIiLUvHlzvf/++8XW1aNHD9lsNo9QtmHDBt166606ceKEDhw44LG+V69ekqScnBxNmzZN9erVU2RkpLp27epxjqKmPs6bN0916tRRdHS0xo0bpxkzZqhjx46FanrooYcUHx+vmjVr6tZbb5XdbpckXXnllfrtt990xx13uEciz9i0aZMuv/xyhYeHq379+po0aZJOnz7t3n78+HENHjxY4eHhaty4sV577bVi+8QsGFEzE3uW9EgbKTpBmrrT6GoAADCNLYkpynU4FWINYvojivZMTynjuH/bjKojTdjos9PNmDFDDz30kJo0aaLq1avr999/18CBAzV//nyFhobqlVde0eDBg7Vr1y41aNCg2PPMnTtXixYt0oMPPqjHH39cN9xwg3777TfVqFGj0L6RkZG6+OKLtX79eo0YMUJSfiCbPn26NmzYoPXr12vs2LHat2+fDh486A5qEydO1I4dO7RixQolJCRo5cqVGjBggLZv367mzZsXaue1117T/Pnz9eSTT+qyyy7TihUr9PDDD6tx48Ye+61fv17x8fFav3699u7dq+HDh6tjx4665ZZb9M4776hDhw4aP368x3TPxMREDRgwQPPmzdOLL76oP/74QxMnTtTEiRP10ksvScq//i0pKUnr16+XzWbTpEmTdPy4n39fyoigBgAATO+ON37Q0bRsxcWE6au7ehtdDswo47iUnmR0FeflvvvuU9++fd3LNWrUUIcOHdzL999/v1auXKn3339fEydOLPY8Y8aM0ciRIyVJCxYs0GOPPaZvvvlGAwYMKHL/Xr166a233pIk7dixQ9nZ2brooot0xRVXaMOGDRo7dqw2bNigsLAwXXrppTp48KBeeuklHTx4UAkJCZKkadOmafXq1XrppZe0YMGCQm08/vjjuvnmmzV2bP5N82bPnq1PP/1UGRkZHvtVr15dS5culdVqVatWrfTXv/5V69at0y233KIaNWrIarUqOjraPU1TkhYuXKgbbrhBkydPliQ1b95cjz32mHr27KmnnnpKBw8e1Mcff6xvvvlGF198sSTphRdeUOvWrYvtQzMgqJkBD7wGAAA4P1F1Kn2bXbp08VjOyMjQnDlz9NFHH+nIkSPKy8tTVlaWDh48WOJ52rdv734dGRmpmJiYEkePrrzySs2fP19HjhzRhg0b1KNHD1mtVvXs2VNPP/20pPxRtu7duys0NFTbt2+Xw+FQixYtPM6Tk5OjmjWLHvHetWuX/vWvf3msu+SSS/TZZ595rGvbtq2s1oJn1MXHx2v79u0lvt8ff/xRP/30k8d0RpfLJafTqf3792v37t0KDg5W586d3dtbtWpV5rtS+htBDQAAAOXy5re/69MdR5XncOr48SBFJP1qXDE+nIJolMhIz2fpTps2TWvWrNFDDz2kZs2aKTw8XNdff71yc3NLPI/NZvNYtlgscjqdxe5/2WWXKSQkROvXr9f69evVs2dPSdLFF1+s5ORk7du3Txs2bNCECRMk5QdIq9Wqbdu2eYQqyfOmKOVR1trP1DNhwgRNmjSp0LYGDRpUyN0n/YGgZgqMqAEAgMrlaGq2/v3OT2dNDAqSTp1ybw+28tyh87V582aNGTNGQ4YMkZQfSM6+uYevhIeHu28GsnHjRk2fPl1Sfmi69NJL9cILL+j33393X5920UUXyeFw6Pjx47r88su9aqNly5b69ttvdeONN7rXffvtt2WuNSQkRA6Hw2Ndp06dtGPHDjVr1qzIY1q1aqW8vDxt27bNPfVx165dOnXW76sZcddHAAAAlFlyRk6xV2/UiAzR4A4J/i2oCmrevLneeecd/fDDD/rxxx81atSoUkeXyqtXr15asWKFsrOz1alTJ/f6nj176vHHH3ffdESSWrRooRtuuEE33nij3nnnHe3fv1/ffPONFi5cqI8++qjI899222164YUXtGzZMu3Zs0fz5s3TTz/95HHnRm80atRIn3/+uQ4fPqzk5GRJ0r///W99+eWXmjhxon744Qft2bNH7733nvs6vpYtW2rAgAGaMGGCvv76a23btk3jxo0z/eMDCGpmwDVqAID/b+/O46Iq9z+Af4YRGESWFFlGQXFDI2VRQSR3ClwIt1ziXiG3rsvPvFqptyvgFmlKhpnSNcGbS+pNTW9miTKm5hbiboiE25WlIAGFEWSe3x9ez3Vkhk1gBv28X695yZzznOd8z5fHM/PlbEQN2AhvJT7o9gAn5/VFyvxXcHzeAHi7vGDosBq8mJgYvPDCC+jZsyeCg4MRGBioVUTVpn79+qGwsBD+/v5az2/r06cPCgsLpdv4PxIfH49x48Zh9uzZcHNzw9ChQ3Hq1Cm9d6MMDQ3FvHnz8M4778Db2xsZGRkIDw+HQqGoVpwLFy7EtWvX0LZtWzRv3hzAw2vyDh06hCtXrqBXr17w8vJCRESEdKOTR/EqlUr06dMHw4cPx+TJk2Fvb4DrGqtBJgSrhJooKCiAjY0N8vPzYW1t/VR9iVhvyPLSISCDDIK3568HpaWl2Lt3LwYNGlTuXGiqO8y7YTDvhsG8164eHxyo0l0fmff6c+E/+Riy6ggA4A2flvCVX6uXvKvVamRkZMDV1bXaX/KfNRqNBgUFBbC2toaJScM7/vLKK6/A0dERX375paFDKaeicZabmws7O7taqQMqwmvUjAJrZSIiIiJ6dhUVFWHt2rUIDAyEXC7Hli1bkJiYiP379xs6NKPFQo2IiIiIiOqUTCbD3r17sWTJEqjVari5ueHrr79GQECAoUMzWizUjMGjs0/NmgAT9wMm/LUQERER0bPDwsICiYmJhg6jQWFFYEzkjQB7435COhERkSFUdF0aEdGzqOFddUhERERERPSMY6FmVPhgSCIiIiIiYqFmXB7cB5I3AOe2GzoSIiIiIiIyIF6jZgwe3UzkQTGwZ8bD56h1ed2wMRERERmRlYlXUKh+ACtFI8wM6GDocBqckgcaRO6+gJQbd2qtz/sPNLXWFxGVx0KNiIiIjN5XJ29KD7xmoVZ9h678hi0nb9ZZ/4pGcj4WlqiW8dRHo8A9GxEREdWd/OJS6edGJjIoTE1q7dXBoQlGeCsNuHVkSOHh4Rg6dGi1lmndujVWrlxZJ/E86dq1a5DJZDhz5ky9rK828YgaERER0XMk6jV3/KlHq1rts7S0FFdrtcdnj0xW8U3jIiMjERUVVaO+5XI5du7cWWHB1KNHD3h6emLt2rXStLVr12LKlCmIj49HeHi4ND08PBzp6ek4fPhwpev+5JNPIETtHnS4du0aXF1dkZKSAk9Pz1rtuyFhoWYManlwExERNXRvf5WCE7/mSe9zCtUGjIbo6WVmZko/b926FREREUhNTZWmNWnSpE7X369fP+zcuVNrWlJSEpydnaFSqbQKNZVKhbCwsCr1a2NjU5th0mN46iMREREZnbx7JcgqUEsvzX//pmlpLjdsYEQ15OjoKL1sbGwgk8m0pn311Vfo1KkTFAoFOnbsiM8++0xatqSkBNOnT4eTkxMUCgVatWqF6OhoAECXLl0AAMOGDYNMJkPr1q11rr9fv35ITU1FVlaWNO3QoUOYO3cuVCqVNC0jIwPXr19Hv379AAA3b97EqFGjYGtri6ZNmyIkJATXrl2T2j956mNhYSFCQ0NhaWkJJycnfPzxx+jbty9mzpypFU9RURHGjx8PKysruLi44PPPP5fmubq6AgC8vLwgk8nQt29fad66dev05gkATp48CS8vLygUCnTr1g0pKSm6fyENAI+oGQUeUSMiInpcU0szOFortKZZmssx+1U3A0VEDcJPnwLHVlfezskDeOMr7WmbxwCZZytf1m8a0HN6zeLTY9OmTYiIiMCnn34KLy8vpKSkYNKkSbC0tERYWBhiY2Oxe/dubNu2DS4uLrh58yZu3nx4c5iDBw+iffv2iI+PR1BQEORy3X/M8Pf3h6mpKZKSkjB27FhcunQJxcXFmDBhAubMmYOMjAy4uroiKSkJCoUCfn5+KC0tRWBgIPz8/HD48GE0atQIixcvRlBQEM6dOwczM7Ny65k1axaOHj2K3bt3w8HBARERETh9+nS5UxhXrFiBRYsW4W9/+xv+9a9/YcqUKejTpw/c3Nxw8uRJ+Pj4IDExEe7u7tJ6KsvT3bt3MWTIELzyyivYuHEjMjIy8Pbbb9fq76o+sVAjIiIio/PJGC9Dh0AN0f1CoPB25e1sWpSfVvR71Za9X1j9uCoRGRmJFStWYPjw4QAeHlG6dOkS4uLiEBYWhhs3bqB9+/Z4+eWXIZPJ0KrVw2sMNRoN7OzsAAC2trZwdHTUuw5LS0v4+PhApVJh7NixUKlUePnll2Fubo6ePXtCpVLB1dUVKpUKfn5+MDc3x8aNG6HRaLBu3TrpGrv4+HjY2tpCpVLh1Vdf1VpHYWEhNmzYgM2bN2PAgAFSe6Wy/M1mBg0ahKlTpwIA5syZg48//hhJSUlwc3ND8+bNAQDNmjXT2qbK8rR582ZoNBp88cUXUCgUcHd3x61btzBlypTq/1KMAAs1IiIiIno2mFs9fB5tZRrb6Z5WlWXNraofVwXu3buH9PR0TJgwAZMmTZKmP3jwQLr+Kzw8HK+88grc3NwQFBSEIUOGlCuSqqJv377Yvn07gIfXoT06pbBPnz5QqVR48803oVKppDjOnj2Lq1evwspKe5vVajXS09PL9f/rr7+itLQUPj4+0jQbGxu4uZU/Ev7olE0A0mmgOTk5emOvSp4uX76MLl26QKH439F4Pz8/vX0aOxZqxuDRzURkJkATR6CJvWHjISIiMpCxnx/H73fvw66JObZM7mHocKih6Tm95qclPnkqZD25e/cuAOAf//gHfH19teY9Oo3R29sbGRkZ+O6775CYmIhRo0YhICAA27Ztq9a6+vXrhyVLluA///kPVCoV3nnnHQAPC7W4uDikp6fj5s2b6N+/vxRb165dsWnTpnJ9PTrqVVOmpqZa72UyGTQa/Q9Rr0qenjUs1IyJRVNg9mVDR0FERGQwGb/fQ1aBGoXqB4YOhaheODg4QKlU4tdff0VoaKjedtbW1hg9ejRGjx6NkSNHIigoCHl5eWjUqBFMTU1RVlZW6bp69uwJMzMzfPbZZ1Cr1ejatSsAoHv37vjtt9+wfv166RRJ4GGBuHXrVtjb28Pa2rrS/tu0aQNTU1OcOnUKLi4uAID8/HxcuXIFvXv3rko6AEC6Ju3xbapKnjp16oQvv/wSarVaOqp2/PjxKq/X2PCuj0aBNxMhIiIiel4tWLAA0dHRiI2NxZUrV3D+/HnEx8cjJiYGABATE4MtW7bgl19+wZUrV7B9+3Y4OjrC1tYWwMMHSB84cABZWVn4448/9K7HwsICPXr0wKpVq+Dv7y8diTIzM9Oa/uhoV2hoKOzs7BASEoLDhw8jIyMDKpUKM2bMwK1bt8r1b2VlhbCwMLz77rtISkrCxYsXMWHCBJiYmFT6HLnH2dvbw8LCAvv27UN2djby8/OrlKc33ngDMpkMkyZNwqVLl7B3714sX768yus1NizUjICs4D///aHqA5iIiIjocerSMrwZfxIeC34o95q/64Khw6MKTJw4EevWrUN8fDw6d+6MPn36ICEhQbpNvZWVFZYtW4Zu3bqhe/fuuHbtGvbu3QsTk4df5T/66CPs378fzs7O8PKq+EY8/fr1Q2FhodYt74GHpz8WFhZKt+UHgMaNG+PHH3+Ei4sLhg8fjk6dOmHChAlQq9V6j7DFxMTAz88PQ4YMQUBAAPz9/aXb6VdVo0aNEBsbi7i4OCiVSoSEhFQpT02aNMGePXtw/vx5eHl54f3338fSpUurvF5jIxO1/Sjx50RBQQFsbGyQn59fpUPBFRELXoBMaCBkcsgi8ypfgJ5aaWkp9u7di0GDBpU7R5rqDvNuGMy7YTDvNdPjgwPIKlDD0VqB438bUO3ln+e8/3AxC5O/TK603SdjPBHiqeOuh0+hPvOuVqulW8lX58v/s0ij0aCgoADW1tZS0WZs7t27hxYtWmDFihWYMGGCocOpsorGWW5uLuzs7GqlDqgIr1EzBgoboPgPyEQZsG0cYPECEPyJoaMiIiKiBqS49H/X8zSzNIONRfmCyb2FDV550aE+w6LnTEpKCn755Rf4+PggPz8fCxcuBADpqBhVHQs1IyJkcsgufVO1W8MSERER6TG9fzu86e9q6DDoObV8+XKkpqbCzMwMXbt2xeHDh6XnvVHVsVAzBjz7lIiIiIieAV5eXkhOrvwUXKqccZ7MSkRERERE9BxjoUZEREREDQ7vh0d1yRjGF099NAqGHwhERETGYMaA9igqeYDGZvyKQro9uqtkUVERLCwsDBwNPauKiooAwKB3j+VekIiIiIzGG74uhg6BjJxcLoetrS1ycnIAPHzWV3Uepvws0Wg0KCkpgVqtNtrb8zc0QggUFRUhJycHtra20kPBDYGFmjGRgQfXiIiIiCrh6OgIAFKx9rwSQqC4uBgWFhbPbbFaV2xtbaVxZigs1IyBEZwDS0RERNRQyGQyODk5wd7eHqWlpYYOx2BKS0vx448/onfv3s/dA97rkqmpqUGPpD3CQo2IiIiMRk6BGmVCQC6Twd5aYehwakyjEVA/KKu8YS26/0BTr+szBnK53Ci+UBuKXC7HgwcPoFAoWKg9g4y6UCsrK0NUVBQ2btyIrKwsKJVKhIeH4+9//7t0eFcIgcjISPzjH//AnTt34O/vjzVr1qB9+/YV9r169Wp89NFHyMrKgoeHB1atWgUfH5/62Cz9zKyATsGAha1h4yAiIjKQ1z49iqwCNRytFTj+twGGDqdGbuYVIXTdCdzIKzJ0KETUgBn1VYdLly7FmjVr8Omnn+Ly5ctYunQpli1bhlWrVkltli1bhtjYWKxduxYnTpyApaUlAgMDoVar9fa7detWzJo1C5GRkTh9+jQ8PDwQGBhowPOc/3vqo2VzIORT4NXFBoqDiIiIntb3F7MMXqQ1a2Ju0PUT0dMz6iNqP/30E0JCQjB48GAAQOvWrbFlyxacPHkSwMOjaStXrsTf//53hISEAAD++c9/wsHBAbt27cKYMWN09hsTE4NJkybhzTffBACsXbsW3377LdavX4+5c+fWw5YRERHRs6q07H/Xnnd0tEKzJmb1uv4Xnazx6osO9bpOIqp9Rl2o9ezZE59//jmuXLmCDh064OzZszhy5AhiYmIAABkZGcjKykJAQIC0jI2NDXx9fXHs2DGdhVpJSQmSk5Mxb948aZqJiQkCAgJw7NgxvbHcv38f9+/fl97n5+cDAPLy8p76ItZGxRrISgU0xQ9Qlpv7VH1R1ZSWlqKoqAi5ubk8p7seMe+GwbwbBvNeMw/Ud6G5X4IH6gfIrcFnojHk/W5BPjT3Hx5Rm9i9Lfp1bF7vMdwruIN79bg+Y8j784h5N4y8vDwAdf9QbKMu1ObOnYuCggJ07NgRcrkcZWVlWLJkCUJDQwEAWVlZAAAHB+2/Gjk4OEjznvT777+jrKxM5zK//PKL3liio6OxYMGCctNdXV2rtU0VOwvMsavF/oiIiBqmmwDsFhk6iqf3+kpDR0BEdSU3Nxc2NjZ11r9RF2rbtm3Dpk2bsHnzZri7u+PMmTOYOXMmlEolwsLC6jWWefPmYdasWdJ7jUaDvLw8NGvW7KmfW1FQUABnZ2fcvHkT1tbWTxsqVQFzbhjMu2Ew74bBvBsG824YzLthMO+GkZ+fDxcXFzRt2rRO12PUhdq7776LuXPnSqcwdu7cGdevX0d0dDTCwsKkh9BlZ2fDyclJWi47Oxuenp46+7Szs4NcLkd2drbW9Ozs7Aofamdubg5zc+0Lc21tbWuwVfpZW1vzP1k9Y84Ng3k3DObdMJh3w2DeDYN5Nwzm3TBMTOr2voxGfdfHoqKicgmQy+XQaB4+J8TV1RWOjo44cOCANL+goAAnTpyAn5+fzj7NzMzQtWtXrWU0Gg0OHDigdxkiIiIiIqL6ZNRH1IKDg7FkyRK4uLjA3d0dKSkpiImJwfjx4wE8fCr9zJkzsXjxYrRv3x6urq6YP38+lEolhg4dKvUzYMAADBs2DNOnTwcAzJo1C2FhYejWrRt8fHywcuVK3Lt3T7oLJBERERERkSEZdaG2atUqzJ8/H1OnTkVOTg6USiXeeustRERESG3ee+893Lt3D5MnT8adO3fw8ssvY9++fVAoFFKb9PR0/P7779L70aNH47fffkNERASysrLg6emJffv2lbvBSH0xNzdHZGRkuVMrqe4w54bBvBsG824YzLthMO+GwbwbBvNuGPWVd5mo6/tKEhERERERUbUY9TVqREREREREzyMWakREREREREaGhRoREREREZGRYaFGRERERERkZFio1YPVq1ejdevWUCgU8PX1xcmTJytsv337dnTs2BEKhQKdO3fG3r176ynSZ0d0dDS6d+8OKysr2NvbY+jQoUhNTa1wmYSEBMhkMq3X43cPpYpFRUWVy1/Hjh0rXIZj/em1bt26XN5lMhmmTZumsz3Hec38+OOPCA4OhlKphEwmw65du7TmCyEQEREBJycnWFhYICAgAGlpaZX2W93Ph+dNRXkvLS3FnDlz0LlzZ1haWkKpVGLcuHG4fft2hX3WZF/1vKlsvIeHh5fLYVBQUKX9crxXrLK869rXy2QyfPTRR3r75HivWFW+L6rVakybNg3NmjVDkyZNMGLECGRnZ1fYb00/E57EQq2Obd26FbNmzUJkZCROnz4NDw8PBAYGIicnR2f7n376CWPHjsWECROQkpKCoUOHYujQobhw4UI9R96wHTp0CNOmTcPx48exf/9+lJaW4tVXX8W9e/cqXM7a2hqZmZnS6/r16/UU8bPB3d1dK39HjhzR25ZjvXacOnVKK+f79+8HALz++ut6l+E4r7579+7Bw8MDq1ev1jl/2bJliI2Nxdq1a3HixAlYWloiMDAQarVab5/V/Xx4HlWU96KiIpw+fRrz58/H6dOnsWPHDqSmpuK1116rtN/q7KueR5WNdwAICgrSyuGWLVsq7JPjvXKV5f3xfGdmZmL9+vWQyWQYMWJEhf1yvOtXle+Lf/3rX7Fnzx5s374dhw4dwu3btzF8+PAK+63JZ4JOguqUj4+PmDZtmvS+rKxMKJVKER0drbP9qFGjxODBg7Wm+fr6irfeeqtO43zW5eTkCADi0KFDetvEx8cLGxub+gvqGRMZGSk8PDyq3J5jvW68/fbbom3btkKj0eicz3H+9ACInTt3Su81Go1wdHQUH330kTTtzp07wtzcXGzZskVvP9X9fHjePZl3XU6ePCkAiOvXr+ttU9191fNOV97DwsJESEhItfrheK+eqoz3kJAQ0b9//wrbcLxXz5PfF+/cuSNMTU3F9u3bpTaXL18WAMSxY8d09lHTzwRdeEStDpWUlCA5ORkBAQHSNBMTEwQEBODYsWM6lzl27JhWewAIDAzU256qJj8/HwDQtGnTCtvdvXsXrVq1grOzM0JCQnDx4sX6CO+ZkZaWBqVSiTZt2iA0NBQ3btzQ25ZjvfaVlJRg48aNGD9+PGQymd52HOe1KyMjA1lZWVrj2cbGBr6+vnrHc00+H6hy+fn5kMlksLW1rbBddfZVpJtKpYK9vT3c3NwwZcoU5Obm6m3L8V77srOz8e2332LChAmVtuV4r7onvy8mJyejtLRUa+x27NgRLi4uesduTT4T9GGhVod+//13lJWVwcHBQWu6g4MDsrKydC6TlZVVrfZUOY1Gg5kzZ8Lf3x8vvfSS3nZubm5Yv349vvnmG2zcuBEajQY9e/bErVu36jHahsvX1xcJCQnYt28f1qxZg4yMDPTq1QuFhYU623Os175du3bhzp07CA8P19uG47z2PRqz1RnPNfl8oIqp1WrMmTMHY8eOhbW1td521d1XUXlBQUH45z//iQMHDmDp0qU4dOgQBg4ciLKyMp3tOd5r34YNG2BlZVXpKXgc71Wn6/tiVlYWzMzMyv3xp7Lv8o/aVHUZfRpVqzVRAzRt2jRcuHCh0nOy/fz84OfnJ73v2bMnOnXqhLi4OCxatKiuw2zwBg4cKP3cpUsX+Pr6olWrVti2bVuV/uJHT++LL77AwIEDoVQq9bbhOKdnUWlpKUaNGgUhBNasWVNhW+6rnt6YMWOknzt37owuXbqgbdu2UKlUGDBggAEje36sX78eoaGhld4MiuO96qr6fbE+8YhaHbKzs4NcLi93Z5js7Gw4OjrqXMbR0bFa7ali06dPx7///W8kJSWhZcuW1VrW1NQUXl5euHr1ah1F92yztbVFhw4d9OaPY712Xb9+HYmJiZg4cWK1luM4f3qPxmx1xnNNPh9It0dF2vXr17F///4Kj6bpUtm+iirXpk0b2NnZ6c0hx3vtOnz4MFJTU6u9vwc43vXR933R0dERJSUluHPnjlb7yr7LP2pT1WX0YaFWh8zMzNC1a1ccOHBAmqbRaHDgwAGtv2g/zs/PT6s9AOzfv19ve9JNCIHp06dj586dOHjwIFxdXavdR1lZGc6fPw8nJ6c6iPDZd/fuXaSnp+vNH8d67YqPj4e9vT0GDx5creU4zp+eq6srHB0dtcZzQUEBTpw4oXc81+Tzgcp7VKSlpaUhMTERzZo1q3Yfle2rqHK3bt1Cbm6u3hxyvNeuL774Al27doWHh0e1l+V411bZ98WuXbvC1NRUa+ympqbixo0besduTT4TKgqQ6tBXX30lzM3NRUJCgrh06ZKYPHmysLW1FVlZWUIIIf785z+LuXPnSu2PHj0qGjVqJJYvXy4uX74sIiMjhampqTh//ryhNqFBmjJlirCxsREqlUpkZmZKr6KiIqnNk7lfsGCB+P7770V6erpITk4WY8aMEQqFQly8eNEQm9DgzJ49W6hUKpGRkSGOHj0qAgIChJ2dncjJyRFCcKzXpbKyMuHi4iLmzJlTbh7Hee0oLCwUKSkpIiUlRQAQMTExIiUlRbq74IcffihsbW3FN998I86dOydCQkKEq6urKC4ulvro37+/WLVqlfS+ss8HqjjvJSUl4rXXXhMtW7YUZ86c0drX379/X+rjybxXtq+iivNeWFgo3nnnHXHs2DGRkZEhEhMThbe3t2jfvr1Qq9VSHxzv1VfZfkYIIfLz80Xjxo3FmjVrdPbB8V49Vfm++Je//EW4uLiIgwcPip9//ln4+fkJPz8/rX7c3NzEjh07pPdV+UyoChZq9WDVqlXCxcVFmJmZCR8fH3H8+HFpXp8+fURYWJhW+23btokOHToIMzMz4e7uLr799tt6jrjhA6DzFR8fL7V5MvczZ86Ufk8ODg5i0KBB4vTp0/UffAM1evRo4eTkJMzMzESLFi3E6NGjxdWrV6X5HOt15/vvvxcARGpqarl5HOe1IykpSec+5VFuNRqNmD9/vnBwcBDm5uZiwIAB5X4frVq1EpGRkVrTKvp8oIrznpGRoXdfn5SUJPXxZN4r21dRxXkvKioSr776qmjevLkwNTUVrVq1EpMmTSpXcHG8V19l+xkhhIiLixMWFhbizp07OvvgeK+eqnxfLC4uFlOnThUvvPCCaNy4sRg2bJjIzMws18/jy1TlM6EqZP/tnIiIiIiIiIwEr1EjIiIiIiIyMizUiIiIiIiIjAwLNSIiIiIiIiPDQo2IiIiIiMjIsFAjIiIiIiIyMizUiIiIiIiIjAwLNSIiIiIiIiPDQo2IiOpdQkICbG1tDR3GU5HJZNi1a1eFbcLDwzF06NB6iUeXqKgoyGQyyGQyrFy58qn66tu3r9TXmTNnaiU+IiLSj4UaERHVSHh4uPTF/fHX1atXDR1avcjMzMTAgQMBANeuXdNZwHzyySdISEio/+Ae4+7ujszMTEyePFmaNmvWLDRt2hTOzs7YtGmTVvvt27cjODi4XD87duzAyZMn6zxeIiJ6qJGhAyAiooYrKCgI8fHxWtOaN29uoGjql6OjY6VtbGxs6iGSijVq1Egr1j179mDz5s344YcfkJaWhvHjxyMwMBB2dnbIz8/H+++/j8TExHL9NG3aFAUFBfUZOhHRc41H1IiIqMbMzc3h6Oio9ZLL5YiJiUHnzp1haWkJZ2dnTJ06FXfv3tXbz9mzZ9GvXz9YWVnB2toaXbt2xc8//yzNP3LkCHr16gULCws4OztjxowZuHfvnt7+oqKi4Onpibi4ODg7O6Nx48YYNWoU8vPzpTYajQYLFy5Ey5YtYW5uDk9PT+zbt0+aX1JSgunTp8PJyQkKhQKtWrVCdHS0NP/xUx9dXV0BAF5eXpDJZOjbty8A7VMfP//8cyiVSmg0Gq1YQ0JCMH78eOn9N998A29vbygUCrRp0wYLFizAgwcPAABCCERFRcHFxQXm5uZQKpWYMWOG3jzocvnyZfTt2xfdunXD2LFjYW1tjYyMDADAe++9hylTpsDFxaVafRIRUe1joUZERLXOxMQEsbGxuHjxIjZs2ICDBw/ivffe09s+NDQULVu2xKlTp5CcnIy5c+fC1NQUAJCeno6goCCMGDEC586dw9atW3HkyBFMnz69whiuXr2Kbdu2Yc+ePdi3bx9SUlIwdepUaf4nn3yCFStWYPny5Th37hwCAwPx2muvIS0tDQAQGxuL3bt3Y9u2bUhNTcWmTZvQunVrnet6dEpgYmIiMjMzsWPHjnJtXn/9deTm5iIpKUmalpeXh3379iE0NBQAcPjwYYwbNw5vv/02Ll26hLi4OCQkJGDJkiUAgK+//hoff/wx4uLikJaWhl27dqFz584V5uFJHh4e+Pnnn/HHH38gOTkZxcXFaNeuHY4cOYLTp09Xu/AjIqI6IoiIiGogLCxMyOVyYWlpKb1Gjhyps+327dtFs2bNpPfx8fHCxsZGem9lZSUSEhJ0LjthwgQxefJkrWmHDx8WJiYmori4WOcykZGRQi6Xi1u3bknTvvvuO2FiYiIyMzOFEEIolUqxZMkSreW6d+8upk6dKoQQ4v/+7/9E//79hUaj0bkOAGLnzp1CCCEyMjIEAJGSkqLVJiwsTISEhEjvQ0JCxPjx46X3cXFxQqlUirKyMiGEEAMGDBAffPCBVh9ffvmlcHJyEkIIsWLFCtGhQwdRUlKiMyZdefDw8NA5vW3btuKll14SO3bsEPfv3xcvvfSS+Pnnn8WqVatEhw4dRM+ePcWFCxe0ltO3nUREVPt4RI2IiGqsX79+OHPmjPSKjY0F8PDI0oABA9CiRQtYWVnhz3/+M3Jzc1FUVKSzn1mzZmHixIkICAjAhx9+iPT0dGne2bNnkZCQgCZNmkivwMBAaDQa6ZQ9XVxcXNCiRQvpvZ+fHzQaDVJTU1FQUIDbt2/D399faxl/f39cvnwZwMPTFs+cOQM3NzfMmDEDP/zwQ43z9EhoaCi+/vpr3L9/HwCwadMmjBkzBiYmJtK2Lly4UGtbJ02ahMzMTBQVFeH1119HcXEx2rRpg0mTJmHnzp3SaZHVERUVhatXr+L8+fMYNmwYoqOjERAQAFNTUyxevBhHjhzBxIkTMW7cuKfeZiIiqhkWakREVGOWlpZo166d9HJycsK1a9cwZMgQdOnSBV9//TWSk5OxevVqAA+v+9IlKioKFy9exODBg3Hw4EG8+OKL2LlzJwDg7t27eOutt7QKwrNnzyItLQ1t27ats23z9vZGRkYGFi1ahOLiYowaNQojR458qj6Dg4MhhMC3336Lmzdv4vDhw9Jpj8DDbV2wYIHWtp4/fx5paWlQKBRwdnZGamoqPvvsM1hYWGDq1Kno3bs3SktLaxzTL7/8go0bN2LRokVQqVTo3bs3mjdvjlGjRuH06dMoLCx8qm0mIqKa4V0fiYioViUnJ0Oj0WDFihXSkaJt27ZVulyHDh3QoUMH/PWvf8XYsWMRHx+PYcOGwdvbG5cuXUK7du2qFceNGzdw+/ZtKJVKAMDx48dhYmICNzc3WFtbQ6lU4ujRo+jTp4+0zNGjR+Hj4yO9t7a2xujRozF69GiMHDkSQUFByMvLQ9OmTbXWZWZmBgAoKyurMCaFQoHhw4dj06ZNuHr1Ktzc3ODt7S3N9/b2RmpqaoXbamFhgeDgYAQHB2PatGno2LEjzp8/r9VPVQkh8NZbbyEmJgZNmjRBWVmZVPQ9+reybSIiorrBQo2IiGpVu3btUFpailWrViE4OBhHjx7F2rVr9bYvLi7Gu+++i5EjR8LV1RW3bt3CqVOnMGLECADAnDlz0KNHD0yfPh0TJ06EpaUlLl26hP379+PTTz/V269CoUBYWBiWL1+OgoICzJgxA6NGjZJuVf/uu+8iMjISbdu2haenJ+Lj43HmzBnpuWIxMTFwcnKCl5cXTExMsH37djg6Oup8ULe9vT0sLCywb98+tGzZEgqFQu+t+UNDQzFkyBBcvHgRf/rTn7TmRUREYMiQIXBxccHIkSNhYmKCs2fP4sKFC1i8eDESEhJQVlYGX19fNG7cGBs3boSFhQVatWpV4e9En3Xr1qF58+bSc9P8/f0RFRWF48eP47vvvsOLL77Y4B9MTkTUULFQIyKiWuXh4YGYmBgsXboU8+bNQ+/evREdHa33eie5XI7c3FyMGzcO2dnZsLOzw/Dhw7FgwQIAQJcuXXDo0CG8//776NWrF4QQaNu2LUaPHl1hHO3atcPw4cMxaNAg5OXlYciQIfjss8+k+TNmzEB+fj5mz56NnJwcvPjii9i9ezfat28PALCyssKyZcuQlpYGuVyO7t27Y+/evdJRwsc1atQIsbGxWLhwISIiItCrVy+oVCqdcfXv3x9NmzZFamoq3njjDa15gYGB+Pe//42FCxdi6dKlMDU1RceOHTFx4kQAgK2tLT788EPMmjULZWVl6Ny5M/bs2YNmzZpVmAtdsrOzsWTJEvz000/SNB8fH8yePRuDBw+Gvb09NmzYUO1+iYiodsiEEMLQQRAREdWmqKgo7Nq1C2fOnDF0KAZV23m4du0aXF1dkZKSAk9Pz1rpk4iIdOPNRIiIiJ5h58+fR5MmTbSOJtbEwIED4e7uXktRERFRZXjqIxER0TNqxowZ0nVwzZs3f6q+1q1bh+LiYgAPH31ARER1i6c+EhERERERGRme+khERERERGRkWKgREREREREZGRZqRERERERERoaFGhERERERkZFhoUZERERERGRkWKgREREREREZGRZqRERERERERoaFGhERERERkZFhoUZERERERGRk/h+LJiacFUszrwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1200x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘制ROC\n",
    "plot_roc(\"Train Baseline\", train_labels, train_predictions_baseline, color=colors[0])\n",
    "plot_roc(\"Test Baseline\", test_labels, test_predictions_baseline, color=colors[0], linestyle='--')\n",
    "\n",
    "plot_roc(\"Train Weighted\", train_labels, train_predictions_weighted, color=colors[1])\n",
    "plot_roc(\"Test Weighted\", test_labels, test_predictions_weighted, color=colors[1], linestyle='--')\n",
    "\n",
    "\n",
    "plt.legend(loc='lower right');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘制AUPRC\n",
    "plot_prc(\"Train Baseline\", train_labels, train_predictions_baseline, color=colors[0])\n",
    "plot_prc(\"Test Baseline\", test_labels, test_predictions_baseline, color=colors[0], linestyle='--')\n",
    "\n",
    "plot_prc(\"Train Weighted\", train_labels, train_predictions_weighted, color=colors[1])\n",
    "plot_prc(\"Test Weighted\", test_labels, test_predictions_weighted, color=colors[1], linestyle='--')\n",
    "\n",
    "\n",
    "plt.legend();"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 总结\n",
    "由于可供学习的样本过少，不平衡数据的分类是固有难题。我们应该始终先从数据开始，尽可能多地收集样本，并充分考虑可能相关的特征，以便模型能够充分利用占少数的类。有时我们的模型可能难以改善且无法获得想要的结果，因此请务必牢记问题的上下文，并在不同类型的错误之间进行权衡。"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "tensorflow",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.16"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
